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The graph neural network (GNN) has become a promising method to predict molecular properties with end-to-end supervision, as it can learn molecular features directly from chemical graphs in a black-box manner. However, to achieve high prediction accuracy, it is essential to supervise a huge amount of property data, which is often accompanied by a high property experiment cost. Prior to the deep learning method, descriptor-based quantitative structure–property relationships (QSPR) studies have investigated physical and chemical knowledge to manually design descriptors for effectively predicting properties. In this study, we extend a message-passing neural network (MPNN) to include a novel MPNN architecture called the knowledge-embedded MPNN (KEMPNN) that can be supervised together with nonquantitative knowledge annotations by human experts on a chemical graph that contains information on the important substructure of a molecule and its effect on the target property (e.g., positive or negative effect). We evaluated the performance of the KEMPNN in a small training data setting using a physical chemistry dataset in MoleculeNet (ESOL, FreeSolv, Lipophilicity) and a polymer property (glass-transition temperature) dataset with virtual knowledge annotations. The results demonstrate that the KEMPNN with knowledge supervision can improve the prediction accuracy obtained from the MPNN. The results also demonstrate that the accuracy of the KEMPNN is better than or comparable to those of descriptor-based methods even in the case of small training data.

Chemists are trained to recognize aromaticity semi-intuitively, using pictures of resonance structures and Frost-Musulin diagrams, or simple electron-counting rules such as Huckel's 4n + 2/4n rule. To quantify aromaticity one can use various aromaticity indices, each of which is a number reflecting some experimentally measured or calculated molecular property, or some feature of the molecular wavefunction, which often has no visual interpretation or may not have direct chemical relevance. We show that computed isotropic magnetic shielding isosurfaces and contour plots provide a feature-rich picture of aromaticity and chemical bonding which is both quantitative and easy-to-visualize and interpret. These isosurfaces and contour plots make good chemical sense as at atomic positions they are pinned to the nuclear shieldings which are experimentally measurable through chemical shifts. As examples we discuss the archetypal aromatic and antiaromatic molecules of benzene and square cyclobutadiene, followed by modern visual interpretations of Clar's aromatic sextet theory, the aromaticity of corannulene and heteroaromaticity.

Machine learning algorithms have been found to be effective in predicting the properties of molecules and materials. Recently, a new strategy, Δ-machine learning, which uses low-level calculations as a baseline to predict properties of high-level methods, has been proposed to further reduce computational costs. It has been successfully applied to predictions of potential energy surfaces, bandgaps and chemical shieldings. Here we introduce a new descriptor, in which we used harmonic vibrational frequencies as the descriptor in predictions of molecular properties, namely the frequency descriptor (FD). In detail, we used harmonic vibrational frequencies of several semi-empirical methods (the PM6, PM7 and GFN2-xTB methods) as the descriptor in Δ-machine learning. The energies, enthalpies and HOMO–LUMO gaps of 6095 C7H10O2 isomers at high-level calculations were used as target properties to test the descriptor. We found that the FD generated by the GFN2-xTB method has excellent performance among several semiempirical methods. The chemical accuracy can be achieved with a small training set size according to the combination of single-point calculations at density functional theory levels. In addition, we further included infrared intensities to the FD, namely the FD-II by which the chemical accuracy of energies can be achieved with a small training set size (3%) that represents the smallest sample size in the current dataset (C7H10O2 isomers). We expect that the FD and FD-II can also be used to accelerate other property predictions.

Previously, we and other laboratories have reported an unusual and strong Raman optical activity (ROA) induced in solvents by chiral dyes. Various theories of the phenomenon appeared, but they were not capable of explaining fully the observed ROA band signs and intensities. In this work, an analysis based both on the light scattering theory and dedicated experiments provides a more complete understanding. For example, double‐cell magnetic circular dichroism and magnetic ROA experiments with copper‐porphyrin complex show that the induced chirality is observed without any contact of the solvents with the complex. The results thus indicate that a combination of electronic circular dichroism (ECD) with the polarized Raman scattering is responsible for the effect. The degree of circularity of solvent vibrational bands is a principal molecular property participating in the event. The insight and the possibility to predict the chirality transfer promise future applications in spectroscopy, chemical analysis and polarized imaging., In a series of experiments and by a theoretical analysis we decipher Raman optical activity induced in achiral solvents as an interference of two phenomena, circular dichroism and polarized Raman scattering. Applications of this strong effect can be potentially found in analytical chemistry, spectroscopy, and imaging of biologically relevant systems.

Graph neural networks (GNNs) have attracted significant attention from the chemical science community because molecules can be represented as a featured graph. In particular, graph convolutional network (GCN) and its variants have been widely used and have shown a state-of-the-art performance in analyzing molecules, such as molecular label classification, drug discovery, and molecular property prediction. However, in molecular analysis, existing GCNs have two fundamental limitations: (1) information of the molecular scale is distorted and (2) global structures in a molecule are ignored. These limitations can seriously degrade the performance in the machine learning-based molecular analysis because the scale and global structure information of a molecule occasionally have a significant effect on the molecular properties. To overcome the limitations of existing GCNs, we comprehensively analyzed the structure of GCNs and developed a costless solution for the limitations of GCNs. To demonstrate the effectiveness of our solution, extensive experiments were conducted on various benchmark datasets.

The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information. Algebraic graph, specifically, element-specific multiscale weighted colored algebraic graph, embeds complementary 3D molecular information into graph invariants. We propose an algebraic graph-assisted bidirectional transformer (AGBT) framework by fusing representations generated by algebraic graph and bidirectional transformer, as well as a variety of machine learning algorithms, including decision trees, multitask learning, and deep neural networks. We validate the proposed AGBT framework on eight molecular datasets, involving quantitative toxicity, physical chemistry, and physiology datasets. Extensive numerical experiments have shown that AGBT is a state-of-the-art framework for molecular property prediction., Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Here, the authors propose an algebraic graph-assisted bidirectional transformer, which can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy and assisted with 3D stereochemical information from graphs.

Measuring accurate translational self-diffusion coefficients (Dt ) by NMR techniques with modern spectrometers has become rather routine. In contrast, the derivation of reliable molecular information therefrom still remains a nontrivial task. In this paper, two established approaches to estimating molecular size in terms of hydrodynamic volume (VH ) or molecular weight (M) are compared. Ad hoc designed experiments allowed the critical aspects of their application to be explored by translating relatively complex theoretical principles into practical take-home messages. For instance, comparing the Dt values of three isosteric Cp2 MCl2 complexes (Cp=cyclopentadienyl, M=Ti, Zr, Hf), having significantly different molecular mass, provided an empirical demonstration that VH is the critical molecular property affecting Dt . This central concept served to clarify the assumptions behind the derivation of Dt =ƒ(M) power laws from the Stokes-Einstein equation. Some pitfalls in establishing log (Dt ) versus log (M) linear correlations for a set of species have been highlighted by further investigations of selected examples. The effectiveness of the Stokes-Einstein equation itself in describing the aggregation or polymerization of differently shaped species has been explored by comparing, for example, a ball-shaped silsesquioxane cage with its cigar-like dimeric form, or styrene with polystyrene macromolecules.

The molecular property–affinity relationships of dietary flavonoids binding to xanthine oxidase were investigated in vitro by comparing the binding constants obtained from a fluorescence-quenching method. The inhibitions of dietary flavonoids on xanthine oxidase were also investigated and analyzed, revealing that the binding process was influenced by the structural differences of the flavonoids under investigation. For example, methylation and hydroxylation at the 7- and 5-positions weakened the binding affinities, while hydroxylation at the 3- and 3′-positions mostly improved binding affinities. Glycosylation and hydrogenation of the C2C3 double bond also increased affinities for xanthine oxidase. In addition, galloylated catechins showed higher binding affinities than non-galloylated catechins. Trends in the binding affinities and inhibition of flavonoids during structure modifications were summarized. Affinities for xanthine oxidase and inhibition on xanthine oxidase changed in the opposite direction during the methylation and hydroxylation of flavonoids in the A ring, and the glycosylation and hydrogenation of C2C3. However, affinities and inhibition for xanthine oxidase changed in the same direction during the methylation and hydroxylation of flavonoids in the B ring.

The photodegradation of fluazaindolizine in water was investigated under simulated sunlight irradiation. The effects of solution pH, humic acids (HA), nitrates ( NO 3 − ) and Fe(III) ions on photolysis of fluazaindolizine were studied. The results indicated that pH did not significantly affect its photodegradation. At low concentration (up to 5 mg/L), HA slightly facilitated the photodegradation of fluazaindolizine, while at high concentration (10–20 mg/L), HA inhibited its photodegradation. The presence of NO 3 − (0–10 mg/L) and Fe(III) (0–5 mg/L) noticeably accelerated the photodegradation of fluazaindolizine. Moreover, eleven direct transformation products (TPs) were isolated and identified by liquid chromatography quadrupole time-of-flight mass spectrometry. Density functional theory (DFT) calculation was utilized to characterize molecular property of fluazaindolizine and predict the potentiality of the possible photodegradation reaction. Ultimately, a possible transformation mechanism was proposed based on the identified TPs, degradation profiles and DFT calculation. The predominant photoproduct came from ring opening of imidazole-ring and dechlorination. Other TPs resulted from a series of photochemical reactions involving hydroxyl substitution, ring-opening, cleavage, oxidation and decarboxylation. These results were important in elucidating environmental fate of fluazaindolizine in aquatic system and further environmental risk assessment.

Originated from the cyclic delocalization of electrons resulting in extra stability and instability, aromaticity and antiaromaticity are important chemical concepts whose appreciation and quantification are still much of recent interest in the literature. Employing information-theoretic quantities can provide us with more insights and better understanding about them, as we have previously demonstrated. In this work, we examine the triplet-state aromaticity and antiaromaticity, which are governed by Baird's 4n rule, instead of Hückel's 4n + 2 rule for the singlet state. To this end, we have made use of 4 different aromaticity indexes and 8 information-theoretic quantities, examined a total of 22 substituted fulvene derivatives, and compared the results both in singlet and triplet states. It is found that cross-correlations of these two categories of molecular property descriptors enable us to better understand the nature and propensity of aromaticity and antiaromaticity for the triplet state. Our results have not only demonstrated the existence and validity of Baird's rule but also shown that Hückel's rule and Baird's rule indeed share the same theoretical foundation because with these cross-correlation patterns we are able to distinguish them from each other simultaneously in both singlet and triplet states. Our results should provide new insights into the nature of aromaticity and antiaromaticity in the triplet state and pave the road toward new ways to quantify this pair of important chemical concepts.

Depending on the dynamics of the crosslinks, polymer networks can have distinct bulk mechanical behaviors, from viscous liquids to tough solids. Here, by means of designing a crosslink with variable molecular dynamics, we show the control of viscoelasticity of polymer networks in a broad range quantitatively. The hexanoate-isoquinoline@cucurbit[7]uril (HIQ@CB[7]) crosslink exhibits in a combination of protonated and deprotonated states of similar association affinity but distinct molecular dynamics. The molecular property of this crosslink is contributed by linear combination of the parameters at the two states, which is precisely tuned by pH. Using this crosslink, we achieve the quantitative control of viscoelasticity of quasi-ideal networks in 5 orders of magnitude, and we show the reversible control of mechanical response, such as stiffness, strength and extensibility, of tough random polymer networks. This strategy offers a way to tailor the mechanical properties of polymer networks at the molecular level and paves the way for engineering "smart" responsive materials.

Stereochemistry is a fundamental molecular property with important ramifications for structure, function, and activity of organic molecules. The basic building blocks of living organisms (amino acids and sugars) exhibit a precisely selected set of molecular handedness that has evolved over millions of years. The absolute stereochemistry of these building blocks is manifested in the structure and function of the cell machinery (e.g., enzymes, proteins, etc.), which are essential components of life. In the many chemical subdisciplines, molecular stereochemistry is exceedingly important and is often a strong determinant of structure and function. Besides its biological implications, the centrally important role of stereochemistry in many disciplines of chemistry and related fields has led to tremendous effort and activity, highlighted by the success in stereoselective syntheses of a host of functionalities. In the present climate, it is often the difficulty of assigning absolute stereochemistry as opposed to synthesis, which has become a nontrivial challenge, requiring the attention of the community. There will not be a general solution to this problem, as each system will have its own unique requirements and challenges; however, the need for rapid, routine, and microscale analysis is apparent. This is especially true with parallel and high-throughput arrays for screening conditions and catalysts, generating a large number of samples that require analysis.In this Account, we summarize our contribution to this field through the development of molecular receptors for sensing molecular asymmetry. These methodologies strive to unambiguously assign the absolute configuration of asymmetric center(s). To accomplish this task, our laboratory has designed a variety of host molecules, bearing various binding elements, to form stable complexes with chiral molecules (guests). During this complexation event, the stereochemistry of a target molecule induces a supramolecular chirality (i.e., helicity) within the host system. The design of the host system is such that the helicity of the host/guest complex can be observed and assigned via Exciton Coupled Circular Dichroism (ECCD), a nonempirical technique for identifying handedness, which is correlated back to the absolute stereochemistry of the bound chiral molecule. Taking advantage of the high sensitivity of chiroptical techniques (in terms of the required amount of sample for analysis) and fast response time, these methodologies offer a microscale, rapid, and nonempirical solution for assignment of absolute stereochemistry.The first part of this Account describes application of porphyrin tweezers as reporters of chirality for the absolute stereochemical determination of various classes of organic molecules. This methodology is suitable to report the absolute configuration of organic molecules that contain two binding elements (nitrogen or oxygen based functionalities). In the second part, host systems that do not require two sites of attachment to form ECCD active complexes will be described. This enables the absolute stereochemical assignment of challenging chiral molecules with functional groups lacking routine techniques for analysis.

New antibiotics are needed to battle growing antibiotic resistance, but the development process from hit, to lead, and ultimately to a useful drug takes decades. Although progress in molecular property prediction using machine-learning methods has opened up new pathways for aiding the antibiotics development process, many existing solutions rely on large data sets and finding structural similarities to existing antibiotics. Challenges remain in modeling unconventional antibiotic classes that are drawing increasing research attention. In response, we developed an antimicrobial activity prediction model for conjugated oligoelectrolyte molecules, a new class of antibiotics that lacks extensive prior structure-activity relationship studies. Our approach enables us to predict the minimum inhibitory concentration for E. coli K12, with 21 molecular descriptors selected by recursive elimination from a set of 5305 descriptors. This predictive model achieves an R2 of 0.65 with no prior knowledge of the underlying mechanism. We find the molecular representation optimum for the domain is the key to good predictions of antimicrobial activity. In the case of conjugated oligoelectrolytes, a representation reflecting the three-dimensional shape of the molecules is most critical. Although it is demonstrated with a specific example of conjugated oligoelectrolytes, our proposed approach for creating the predictive model can be readily adapted to other novel antibiotic candidate domains.

Graph neural networks are able to solve certain drug discovery tasks such as molecular property prediction and de novo molecule generation. However, these models are considered “black-box” and “hard-to-debug”. This study aimed to improve modeling transparency for rational molecular design by applying the integrated gradients explainable artificial intelligence (XAI) approach for graph neural network models. Models were trained for predicting plasma protein binding, hERG channel inhibition, passive permeability, and cytochrome P450 inhibition. The proposed methodology highlighted molecular features and structural elements that are in agreement with known pharmacophore motifs, correctly identified property cliffs, and provided insights into unspecific ligand–target interactions. The developed XAI approach is fully open-sourced and can be used by practitioners to train new models on other clinically relevant endpoints. © 2021 American Chemical Society, Journal of Chemical Information and Modeling, 61 (3), ISSN:1549-9596, ISSN:0095-2338, ISSN:1520-5142

Oral bioavailability (OBA)-related pharmacokinetic properties, such as aqueous solubility, lipophilicity, and intestinal membrane permeability, play a significant role in drug discovery. However, their measurement is usually costly and time-consuming. Therefore, prediction models based on diverse approaches have been established in recent decades. Computational prediction of molecular properties has become an important step in drug discovery, aiming to identify potential drug-like candidates and reduce costs. However, limitations related to dataset capacity and algorithm adaptation still place restrictions on the applicability of the related models. In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. Benchmark datasets of aqueous solubility (log S), lipophilicity (log D), and membrane permeability measured using the Caco-2 cell line (log Papp) were constructed by merging and calibrating experimental data from diverse articles and databases. Then, a novel molecular property prediction model, called a multiembedding-based synthetic network (MESN), was generated by applying a deep learning algorithm based on the synthesis of multiple types of molecular embeddings. MESN achieves performance improvements over other state-of-the-art methods for the prediction of aqueous solubility, lipophilicity, and membrane permeability. Results were also obtained using several other algorithms and independent validation datasets as a control study. Moreover, a dimension reduction analysis (based on t-distributed stochastic neighbor embedding, t-SNE) and an atomic feature similarity analysis showed that the molecular embeddings extracted from the MESN model exhibit good clustering and diversity. Overall, considering the fundamental role of the data and the superior prediction performance of the model, we highlight the applicability of MESN on benchmark datasets for further utility in drug discovery-related molecular property prediction.

Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and where unanticipated imprecision wastes valuable time and resources. The need for UQ is especially acute for neural models, which are becoming increasingly standard yet are challenging to interpret. While several approaches to UQ have been proposed in the literature, there is no clear consensus on the comparative performance of these models. In this paper, we study this question in the context of regression tasks. We systematically evaluate several methods on five benchmark datasets using multiple complementary performance metrics. Our experiments show that none of the methods we tested is unequivocally superior to all others, and none produces a particularly reliable ranking of errors across multiple datasets. While we believe these results show that existing UQ methods are not sufficient for all common use-cases and demonstrate the benefits of further research, we conclude with a practical recommendation as to which existing techniques seem to perform well relative to others.

Topological indices are numerical numbers that represent the topology of a molecule and are calculated from the graphical depiction of the molecule. The importance of topological indices is due to their use as descriptors in QSPR/QSAR modeling. QSPRs (quantitative structure-property relationships) and QSARs (quantitative structure-activity relationships) are mathematical correlations between a specified molecular property or biological activity and one or more physicochemical and/or molecular structural properties. In this paper, we give explicit expressions of some degree-based topological indices of two classes of metal-organic frameworks (MOFs), namely, butylated hydroxytoluene- (BHT-) based metal-organic ( M = Co , Fe, Mn, Cr) (MBHT) frameworks and M 1 TPyP − M 2 (TPyP = 5,10,15,20 -tetrakis(4-pyridyl)porphyrin and M 1 , M 2 = Fe and Co) MOFs.

We argue that when one divides a molecular property into atom-in-a-molecule contributions, one should perform the division based on the property density of the quantity being partitioned. This is opposition to the normal approach, where the electron density is given a privileged role in defining the properties of atoms-in-a-molecule. Because partitioning each molecular property based on its own property density is inconvenient, we design a reference-free approach that does not (directly) refer atomic property densities. Specifically, we propose a stockholder partitioning method based on relative influence of a molecule's atomic nuclei on the electrons at a given point in space. The resulting method does not depend on an "arbitrary" choice of reference atoms and it has some favorable properties, including the fact that all of the electron density at an atomic nucleus is assigned to that nucleus and the fact all the atoms in a molecule decay at a uniform asymptotic rate. Unfortunately, the resulting model is not easily applied to spatially degenerate ground states. Furthermore, the practical realizations of this strategy that we tried here gave disappointing numerical results. © 2017 Wiley Periodicals, Inc.

Molecular Property Diagnostic Suite ( $$\text {MPDS}^{\mathrm{TB}}$$ ) is a web tool ( http://mpds.osdd.net ) designed to assist the in silico drug discovery attempts towards Mycobacterium tuberculosis (Mtb). $$\text {MPDS}^{\mathrm{TB}}$$ tool has nine modules which are classified into data library (1–3), data processing (4–5) and data analysis (6–9). Module 1 is a repository of literature and related information available on the Mtb. Module 2 deals with the protein target analysis of the chosen disease area. Module 3 is the compound library consisting of 110.31 million unique molecules generated from public domain databases and custom designed search tools. Module 4 contains tools for chemical file format conversions and 2D to 3D coordinate conversions. Module 5 helps in calculating the molecular descriptors. Module 6 specifically handles QSAR model development tools using descriptors generated in the Module 5. Module 7 integrates the AutoDock Vina algorithm for docking, while module 8 provides screening filters. Module 9 provides the necessary visualization tools for both small and large molecules. The workflow-based open source web portal, $$\text {MPDS}^{\mathrm{TB}}$$ 1.0.1 can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular. SYNOPSIS: A web-based $$\text {MPDS}^{\mathrm{TB}}$$ Galaxy tool is developed for assessing therapeutic potential of molecules. $$\text {MPDS}^{\mathrm{TB}}$$ is categorized into Data Library, Data Processing and Data Analysis. It can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular.

COX-2 is a widely studied biological target, since its activity is directly related to the inflammation response. The design of COX-2 selective inhibitors is an ongoing topic in drug design. We performed a quantitative structure–activity relationship and docking studies over a series of benzenesulfonamide derivatives on their inhibition towards COX-1 and COX-2, in order to rationalize their selectivity towards COX-2. Constitutional, topological and molecular property descriptors for the QSAR models and molecular docking calculations were employed. The mathematical model highlighted that lipophilic character and size are the most important features for COX-2 inhibition by benzenesulfonamides. A second QSAR model revealed that the dipole moment, the number of hydrogen bond donors and lipophilicity descriptors of benzenesulfonamides are crucial for their binding to COX-1. Moreover, artificial neural networks were employed to improve the prediction power of the COX-1 inhibition QSAR model. In this sense, we proposed new selective potential inhibitors by introducing different halogens into the benzenesulfonamide scaffold, improving their interactions with key residues of COX-2.

Cell adhesion is crucial for immune response, tissue formation, and cell locomotion. The adhesion process is mediated by the specific binding of membrane-anchored receptor and ligand proteins. These adhesion proteins are in contact with the membranes and may generate curvature, which has been shown for a number of membrane proteins to play an important role in membrane remodeling. An important question remains of whether the local membrane curvatures induced by the adhesion proteins affect their binding. We've performed Monte Carlo simulations of a mesoscopic model for membrane adhesion via the specific binding of curvature-inducing receptors and ligands. We find that the curvatures induced by the adhesion proteins do affect their binding equilibrium constant. We presented a theory that takes into account the membrane deformations and protein–protein interactions due to the induced curvatures, and agrees quantitatively with our simulation results. Our study suggests that the ability to induce membrane curvatures represents a molecular property of the adhesion proteins and should be carefully considered in experimental characterization of the binding affinity.

The molecular properties of polymers are greatly influenced by operation parameters during polymerization in reactors. Since operation parameter distributions in reactors also result in molecular property distributions, polymerization bridges the gap between molecular properties and operation parameters. The combination of the computational fluid dynamics technology with the method of moments to form a uniquely coupled model was used to describe multiscale mixing fields in the reactor. The coupled model was validated by open experimental data, and the effects of polymerization kinetics on macroscopic and microscopic fields were investigated numerically. Also, the coupled model was applied to numerically predict the impacts of some key operation conditions on the main macroscopic flow field parameters and polymer molecular properties.

Complex chemical systems consisting of transition metal element(s) are important and attractive research targets in both experimental chemistry and theoretical chemistry. Transition-metal complexes...

The available experimental and theoretical information on gaseous actinide oxides covering both the neutral and the ionic species are reviewed. The ground-state electronic structures of the oxides of An = Th-Cm have been obtained by the well-tested SOCASPT2 method, and therefore they are very likely reliable. The ground electronic states of the oxides of An = Bk-Lr were reported only from B3LYP calculations. The reliability of B3LYP for this molecular property has been demonstrated by the agreement with the SO-CASPT2 results for almost all of the neutral and ionic oxides for An = Th-Cm. Some additional data with proper quality could be obtained on the basis of empirical relationships evaluated from the computed geometries and vibrational frequencies, reliable knowledge of either parameter can enable confident prediction of the other. The compilations are very helpful in both interpreting complex experimental results and for predicting molecular data not available from experiment.

A new phenomenon termed the nonlinear-to-linear elastic transition was observed for the first time in C60 fullerene. Simulated tensile and compressive tests of C60 under the density functional theory framework showed the presence of anisotropy in C60 with different mechanical behaviour of C60 observed under different deformation tests. Nonlinear-to-linear elastic transition was observed during compressive tests which improved the mechanical properties of C60. Nonlinear-to-linear elastic transition was proposed to be a new-scale sub-nanoscale phenomenon and a model equation was proposed for its description.

The molecular ionization and deprotonation of small organic compounds are important molecular properties which are increasingly being calculated and considered in the mechanistic justification of functional coating performance. Often, only a few molecules are examined for any given set of systems, leading to potentially spurious correlations between calculated molecular properties, and experimentally observed trends. In this study the calculated molecular ionization potential, electron affinity, fundamental gap, and deprotonation energies of 28 small organic molecules will be investigated using ab initio methods, to explore the role that in silico simulation could have in predicting the performance of a functional coating. Results will be presented with respect to an experimentally determined measure of functional coating efficacy. It will be shown that there is no apparent correlation between the experimentally determined measure of the functional coating performance and the calculated molecular properties of small organic molecules investigated in this study. These findings cast aspersions on the popularised relationship between a single molecular property and the ultimate efficacy of a functional coating.

Systematic structure–property studies on a series of co-crystals of potential cancer drugs with aliphatic dicarboxylic acids were undertaken. This study reveals that systematic changes to the molecular nature of the co-crystallizing agent combined with control over the way individual building blocks are organized within the crystalline lattice makes it possible to establish predictable links between molecular structure and macroscopic physical properties, such as melting behaviour and aqueous solubility. However, it is not possible to find any notable correlation between physical properties and chemical compositions in the absence of structural consistency.

In electro-optic (EO) materials, realization of large EO coefficients for organic EO materials requires the simultaneous optimization of chromophore first hyperpolarizability, acentric order, molecular shape etc. As these parameters are complicatedly inter-related, thorough analyses are required to understand the dependence of macroscopic EO activity upon chromophore structure and property. Herein, we presented the synthesis of three chromophores containing different conjugated electron-bridges by acidic and alkaline formylation. Electron-rich moieties thiophene and formyl-thiophene in the different positions of chromophores played the different roles of electron-bridge, site-isolator and electron-isolator, generating intriguing property variations of electron distribution of push–pull structure, intramolecular charge-transfer, solvatochromism, microscopic hyperpolarizability and related density functional theory calculation results. In addition these molecular structure–property relationships were rationally related to the EO activities to understand the impact of microscopic molecular property on macroscopic EO activities of materials.

We have searched for chemical transformations that improve drug development-relevant properties within a given class of active compounds, regardless of the compounds they are applied to. For different compound data sets, varying numbers of frequently occurring data set-dependent transformations were identified that consistently induced favorable changes of selected molecular properties. Sequences of compound pairs representing such transformations were determined that formed pathways leading from unfavorable to favorable regions of property space. Data set-dependent transformations were then applied to predict a series of compounds with increasingly favorable property values. By database searching the desired biological activity was detected for several designed molecules or compounds that were very similar to these molecules. Taken together our findings indicate that data set-dependent transformations can be applied to predict compounds that map to favorable regions of molecular property space and retain their biological activity.

While drug discovery scientists take heed of various guidelines concerning drug-like character, the influence of acid/base properties often remains under-scrutinised. Ionisation constants (pK(a) values) are fundamental to the variability of the biopharmaceutical characteristics of drugs and to underlying parameters such as logD and solubility. pK(a) values affect physicochemical properties such as aqueous solubility, which in turn influences drug formulation approaches. More importantly, absorption, distribution, metabolism, excretion and toxicity (ADMET) are profoundly affected by the charge state of compounds under varying pH conditions. Consideration of pK(a) values in conjunction with other molecular properties is of great significance and has the potential to be used to further improve the efficiency of drug discovery. Given the recent low annual output of new drugs from pharmaceutical companies, this review will provide a timely reminder of an important molecular property that influences clinical success.

Numerous studies indicate that there is strong inherent relationship between the chemical characteristics of chemical compounds and drugs (e.g., boiling point and melting point) and their molecular structures. Topological indices defined on these chemical molecular structures can help researchers better understand the physical features, chemical reactivity, and biological activity. Thus, the study of the topological indices on chemical structure of chemical materials and drugs can make up for lack of chemical experiments and can provide a theoretical basis for the manufacturing of drugs and chemical materials. In this paper, we focus on the family of smart polymer which is widely used in anticancer drugs manufacturing. Several topological indices are determined in view of edge dividing methods, and these results remedy the lack of chemical and medicine experiments thus providing the theoretical basis for pharmaceutical engineering.

An algorithm is presented for the estimation of molecular properties over a library built around a scaffold, which has N sites for functionalization with Mi moieties at the ith scaffold site, corresponding to a library of \({\prod_{i=1}^N M_i}\) molecules. The algorithm relies on a series of operations involving (i) synthesis and property measurement of a minimal number of T randomly sampled members of the library, (ii) expression of the observed property in terms of a high-dimensional model representation (HDMR) of the moiety → property map, (iii) optimization of the ordered sequence of moieties on each site to regularize the HDMR map and (iv) interpolation using the map to estimate the properties of as yet unsynthesized compounds. The set of operations is performed iteratively aiming to reach convergence of the predictive HDMR map with as few synthesized samples as possible. Through simulation, the number T of required random molecular samples is shown to scale very favorably with \({T < < \prod^N_{i=1} M_i}\) for cases up to N = 20 and Mi = 20. For example, high estimation quality was attained for simulated libraries with T ~ 5,000 sampled compounds for a library of 2012 members and T ~ 12,500 sampled compounds for a library of 2020 members. The algorithm is based on the assumption that a systematic pattern exists in the moiety → property map provided that the moieties are optimally ordered on the scaffold sites within the context of HDMR. The overall procedure is referred to as the substituent reordering HDMR algorithm (SR-HDMR). The technique was also successfully tested with laboratory data for estimating C13-NMR shifts in a tri-substituted benzene library and for lac operon repression binding.

The Mendeleev periodic table of atoms is one of the most important principles in natural science. However, there is shortage of analog for molecules. Here we propose two periodic tables, one for diatomic molecules and one for triatomic molecules. The form of the molecular periodic tables is analogous to that of Mendeleev periodic table of atoms. In the table, molecules are classified and arranged by their group number G, which is the number of valence electrons, and the periodic number P, which represents the size of the molecules. The basic molecular properties, including bond length, binding energy, force constant, ionization potential, spin multiplicity, chemical reactivity, and bond angle, change periodically within the tables. The periodicities of diatomic and triatomic molecules are thus revealed. We also demonstrate that the periodicity originates from the shell-like electronic configurations of the molecules. The periodic tables not only contain free molecules, but also the “virtual” molecules present in polyatomic molecules. The periodic tables can be used to classify molecules, to predict unknown molecular properties, to understand the role of virtual molecules in polyatomic molecules, and to initiate new research fields, such as the periodicities of aromatic species, clusters, or nanoparticles. The tables should be of interest not only to scientists in a variety of disciplines, but also to undergraduates studying natural sciences.

Review of ab initio wave-function based approaches for calculation of molecular (response) properties

Cytochrome P450 inhibitory promiscuity of a drug has potential effects on the occurrence of clinical drug-drug interactions. Understanding how a molecular property is related to the P450 inhibitory promiscuity could help to avoid such adverse effects. In this study, an entropy-based index was defined to quantify the P450 inhibitory promiscuity of a compound based on a comprehensive data set, containing more than 11,500 drug-like compounds with inhibition against five major P450 isoforms, 1A2, 2C9, 2C19, 2D6, and 3A4. The results indicated that the P450 inhibitory promiscuity of a compound would have a moderate correlation with molecular aromaticity, a minor correlation with molecular lipophilicity, and no relations with molecular complexity, hydrogen bonding ability, and TopoPSA. We also applied an index to quantify the susceptibilities of different P450 isoforms to inhibition based on the same data set. The results showed that there was a surprising level of P450 inhibitory promiscuity even for substrate specific P450, susceptibility to inhibition follows the rank-order: 1A2 > 2C19 > 3A4 > 2C9 > 2D6. There was essentially no correlation between P450 inhibitory potency and specificity and minor negative trade-offs between P450 inhibitory promiscuity and catalytic promiscuity. In addition, classification models were built to predict the P450 inhibitory promiscuity of new chemicals using support vector machine algorithm with different fingerprints. The area under the receiver operating characteristic curve of the best model was about 0.9, evaluated by 5-fold cross-validation. These findings would be helpful for understanding the mechanism of P450 inhibitory promiscuity and improving the P450 inhibitory selectivity of new chemicals in drug discovery.

Transfer of molecular property tensors (force field, dipole derivatives, polarizabilities, etc.) from smaller fragments to bigger molecules is powerful tool to calculate molecular vibrational spectra. However, we found serious accuracy limits for valinomycin (Phys. Chem. Chem. Phys. 2010, 12, 11021), where the transfer of the Raman optical activity tensors (ROA) had to be avoided. Therefore, in this study, the individual polarizable group model is analyzed for a model water dimer, and the corrections stemming from mutual group polarizations neglected in the transfer are estimated ab initio. The electric dipole polarizability was found more local and less sensitive to the interaction of distant molecular parts than the optical activity tensors (G′, A), which can partially explain the error observed during the transfer. In the second part of the study, tensor derivatives are transferred from smaller fragments to model valinomycin and insulin molecules, and the resultant tensor derivatives and ROA spectra compared to benchmark computations. The results confirmed that the error is caused by mutual polarization of molecular parts, more significant in insulin than in valinomycin, and could only partially be improved by increased size of the fragments.

Previous studies of the analysis of molecular matched pairs (MMPs) have often assumed that the effect of a substructural transformation on a molecular property is independent of the context (i.e., the local structural environment in which that transformation occurs). Experiments with large sets of hERG, solubility, and lipophilicity data demonstrate that the inclusion of contextual information can enhance the predictive power of MMP analyses, with significant trends (both positive and negative) being identified that are not apparent when using conventional, context-independent approaches.

A theoretical investigation based on DFT predictions on 1,2-dithienylperfluorocyclopentene derivatives is presented. Molecules of this class have been demonstrated to show efficient thermally irreversible photochromism for developing optical switches. In particular, by doping a glassy polymer matrix with 1,2-dithienylperfluorocyclopentene derivatives, an optical element with a light induced modulation of the refractive index can be obtained. Here, a simple model is proposed allowing the correlation between the change of the molecular polarizability, upon photoisomerization, to the modulation of the refractive index of the bulk material. Based on this model and on DFT simulations, we suggest criteria for the optimization and design of the bulk material property (i.e. refractive index modulation ) by modifying, with suitable chemical substitutions, the molecular and electronic structure of the photochromic species (that is by changing the molecular property , i.e. the molecular polarizability ). A systematic analysis of the polarizability changes upon photoisomerization (from the uncolored to the colored form) is carried out for 24 different molecules and correlations with the changes of the electronic and molecular structure are proposed and discussed.

As a comparative study, the extracytoplasmic domain of human CD83 (hCD83ext) was expressed as a glutathione-S-transferase (GST) fusion in two Escherichia coli B strains, i.e., BL21 and Origami B, respectively, with a reductive and oxidative cytoplasm. The final therapeutic products of hCD83ext produced by the two expression hosts exhibited significant differences in protein conformation and molecular property, which presumably resulted from different disulfide patterns. The study highlights the importance of developing proper host/vector systems and biomanufacturing conditions for the production of recombinant therapeutic proteins with a consistent product quality.

In this work, property clustering techniques and group contribution methods are combined to enable simultaneous consideration of process performance requirements and molecular property constraints. Using this methodology, the process design problem is solved to identify the property targets corresponding to the desired process performance. A significant advantage of the developed methodology is that for problems that can be satisfactorily described by only three properties, the process and molecular design problems can be simultaneously solved visually on a ternary diagram, irrespective of how many molecular fragments are included in the search space. On the ternary cluster diagram, the target properties are represented as individual points if given as discrete values or as a region if given as intervals. The structure and identity of candidate components is then identified by combining or “mixing” molecular fragments until the resulting properties match the targets.

A critically evaluated database of human oral bioavailability for 768 chemical compounds is described in this study (http://modem.ucsd.edu/adme), which provides the scientific community a publicly available and reliable source for developing predictive models of human oral bioavailability. The correlations between several important molecular properties and human oral bioavailability were investigated and compared with an earlier report by analyzing the rat oral bioavailability data (J. Med. Chem. 2002, 45, 2615). We showed that the percentages of compounds meeting the criteria based on molecular properties does not distinguish compounds with poor oral bioavailability from those with acceptable values, which may suggest that no simple rule based on molecular properties can be used as general filters to predict oral bioavailability with high confidence. A data set of intestinal absorption was also examined and compared with that of oral bioavailability. The performance of these rules based on molecular properties in the prediction of intestinal absorption is obviously much better than that of oral bioavailability in term of false positive rate, and, therefore, the applications of the "rule-based" approaches on the prediction of human bioavailability should be very cautious.

The concept of compound class-specific profiling and scaling of molecular fingerprints for similarity searching is discussed and applied to newly designed fingerprint representations. The approach is based on the analysis of characteristic patterns of bits in keyed fingerprints that are set on in compounds having equivalent biological activity. Once a fingerprint profile is generated for a particular activity class, scaling factors that are weighted according to observed bit frequencies are applied to signature bit positions when searching for similar compounds. In systematic similarity search calculations over 23 diverse activity classes, profile scaling consistently increased the performance of fingerprints containing property descriptors and/or structural keys. A significant improvement of approximately 15% was observed for a new fingerprint consisting of binary encoded molecular property descriptors and structural keys. Under scaling conditions, this fingerprint, termed MP-MFP, correctly recognized on average close to 60% of all active test compounds, with only a few false positives. MP-MFP outperformed MACCS keys and other reference fingerprints. In general, optimum performance in scaling calculations was achieved at higher threshold values of the Tanimoto coefficient than in nonscaled calculations, thereby increasing the search selectivity. In general, putting relatively high weight on signature bit positions that were always, or almost always, set on was found to be the most effective scaling procedure. Analysis of class-specific search performance revealed that profile scaling of MP-MFP improved the similarity search results for each of the 23 activity classes.

The median partitioning (MP) method was originally developed for the selection of diverse subsets from compound databases. Following this approach, property descriptors are used in subsequent steps to divide compounds into defined partitions from which representative molecules are selected. For descriptor analysis, MP was coupled to a genetic algorithm. MP subset selection does not depend on pairwise comparison of molecules and is therefore applicable to very large compound pools. Here the MP approach was evaluated for the classification of molecules according to biological activity. A total of 317 molecules belonging to 21 different activity classes were studied. MP compound classification calculations were carried out both in the presence and absence of 2000 randomly selected "background" molecules. The performance of MP was compared to cell-based partitioning and found to be at least comparable, with up to approximately 82% of active molecules occurring in "pure" partitions consisting only of molecules sharing the same activity. Different from cell-based methods, MP classification is based on "direct" and "sequential" contributions of molecular property descriptors. Our results suggest that MP in not only an effective method for the selection of diverse subsets but also for the classification of active compounds and searching for molecules with desired activity.

The effect of one or several substituents X i on a molecular property f (reaction rate, spectroscopic property, etc.) is often described in terms of substituent parameters λ i (X) depending on the nature X of the substituent, and the position i of substitution. We call self-parametrization the case when the substituents are parameterized by the value λ i (X)=f i (X) of f itself in the molecule monosubstituted by X in position i. On the example of the linear plus cross-term models, we show that self-parametrization implies that the coefficients of the model are interdependent. The theoretical relations between them are given, for equivalent or nonequivalent positions of substitution, and tested on measured acid–base equilibrium constants, and NMR coupling constants.

In this work we have quantitatively analyzed substituent effects on the homolytic bond dissociation enthalpy of 79 different compounds using a method based on discrete distance dependent atomic contributions to a molecular property. The resulting empirical relationship can be used to predict C-H bond dissociation enthalpies (within +/-10 kJ mol(-1)) for molecules where resonance contributions and captodative stabilization are insignificant. For species where captodative stabilization of the corresponding C-centered radical is possible, the method clearly overestimates the C-H bond dissociation enthalpy.

Combinatoric manipulative methodology to characterize different isomer-class features is extended and illustrated in application to alkane isomers. Stereoisomeric enumerations and various molecular−property characteristics for structural-isomer classes are made. For a range of carbon-atom counts N we enumerate the following: the mean number of diastereomers per N-carbon structural isomer; the mean number of stereoisomers per N-carbon diastereomer; and the mean number of “conformers” per N-carbon stereoisomer. Also we consider distributions of several graph-theoretic properties, characterizing the distributions in terms of averages, variances, covariances, and extremal values. The results for these graph-theoretic invariants are used to predict (via substructural cluster expansion) some molecular properties (heats of formation and magnetic susceptibilities) averaged over these classes. The associated proper standard deviations as well as the extremal values for these cluster-expansion approximants are als...

Numerous correlations have been developed between the organic carbon/water partition coefficient KOC and various molecular properties and descriptors, but most notably the octanol/water partition coefficient KOW and water solubility. From an analysis of the theory underlying in this partitioning and an examination of the existing database, it is suggested that the preferred approach is to correlate the quantity log(KOC/KOW), which is essentially the ratio of activity coefficients in octanol and organic carbon, with a molecular property or with log KOW rather than log KOC with log KOW. Such an approach is presented for estimating KOC for hydrophobic chemicals, including an expression of uncertainty limits, based on a correlation derived between log(KOC/KOW) and log KOW. In its simplest form the correlation is that KOC = 0.35KOW subject to variation by a factor of 2.5 in either direction. It is suggested that new experimental data be assessed for consistency and achievement of true equilibrium by comparison...