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Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery, which requires the optimization of a specific objective based on satisfying chemical rules. Herein, we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated molecule. The Matched Molecular Pairs (MMPs), which contain the source and target molecules, are used herein, and logD and solubility are selected as the optimization properties. The main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix dimension. Threshold intervals and state changes are then used to encode logD and solubility for subsequent tests. During the experiments, we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365, 1503, and 1570 MMPs as the training, validation, and test sets, respectively. Transformer models are compared with the baseline models with respect to their abilities to generate molecules with specific properties. Results show that the transformer model can accurately optimize the source molecules to satisfy specific properties.

To study the effects of drugs on embryo/fetal development (EFD), developmental and reproductive toxicity studies in zebrafish (Danio rerio) embryos is expected to be an accepted alternative method to animal studies using mammals. However, there is a lack of clarity in the relationship between the concentration of developmental toxicity agents in whole embryos or larvae (Ce) and that in aqueous solution (Cw), and also between the amount of drug exposure required to cause developmental toxicity in zebrafish embryos or larvae and that required in mammals. Here, we measured Ce for developmental toxicity agents every 24 h starting at 24 h post fertilization (hpf). We found a high correlation (R2: 0.87–0.96) between log [Ce/Cw] and the n-octanol–water distribution coefficient at pH 7 (logD) of each drug at all time points up to 120 hpf. We used this relationship to estimate the Ce values of the 21 positive-control reference drugs listed in ICH guidelines on reproductive and developmental toxicity studies (ICH S5). We then calculated the area under the Ce–time curve in zebrafish (zAUC) for each drug from the regression equation between log [Ce/Cw] and logD and compared it with the AUC at the no-observed-adverse-effect level in rats and rabbits and at the effective dose in humans described in ICH S5. The log of the calculated zAUC for the 14 drugs identified as positive in the zebrafish developmental toxicity test was relatively highly positively correlated with the log [AUC] for rats, rabbits, and humans. These findings provide important and positive information on the applicability of the zebrafish embryo developmental toxicity test as an alternative method of EFD testing. (267 words)

Based on salting-out assisted liquid-liquid extraction (SALLE) and high performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS), a simple, rapid pretreatment without complex clean-up for the determination of 22 veterinary drug residues in aquatic products was developed and validated. In order to improve the efficiency of the method, the key procedural parameters of SALLE were fabricated. Na 2 EDTA-Mcllvaine buffer/ACN was used as the extraction solvent, anhydrous MgSO 4 and NaCl as the extraction salts. The relationship between extraction efficiency and logD was initially evaluated during the optimization process. This study was well validated in various aquatic samples such as bass, large yellow croaker, carp, and shrimp, the limits of detection (LOD) and accuracy for all compounds ranged from 0.5 to 1.0 μg/kg, 71.4% to 120%. This method has the advantages of rapidity, simplicity, low cost, and high efficiency, and has broad potential for risk monitoring and evaluation of veterinary antibiotics in aquatic products. • A multi-residue determination of 22 drug residue in aquatic product is presented. • A simple SALLE combined with a HPLC–MS/MS analysis is proposed. • The relationship between logD and drug extraction efficiency was initially explored. • The specificity, linearity, accuracy and precision of the method are fully verified. [ABSTRACT FROM AUTHOR]

In drug discovery, partition and distribution coefficients, logP and logD for octanol/water, are widely used as metrics of the lipophilicity of molecules, which in turn have a strong influence on the bioactivity and bioavailability of potential drugs. There are a variety of established methods, mostly fragment or atom-based, to calculate logP while logD prediction generally relies on calculated logP and pKa for the estimation of neutral and ionized populations at a given pH. Algorithms such as ClogP have limitations generally leading to systematic errors for chemically related molecules while pKa estimation is generally more difficult due to the interplay of electronic, inductive and conjugation effects for ionizable moieties. We propose an integrated machine learning QSAR modeling approach to predict logD by training the model with experimental data while using ClogP and pKa predicted by commercial software as model descriptors. By optimizing the loss function for the ClogD calculated by the software, we build a correction model that incorporates both descriptors from the software and available experimental logD data. Additionally, we calculate logP from the logD model using the software predicted pKa's. Here, we have trained models using publicly or commercial available logD data to show that this approach can improve on commercial software predictions of lipophilicity. When applied to other logD data sets, this approach extends the domain of applicability of logD and logP predictions over commercial software. Performance of these models favorably compare with models built with a larger set of proprietary logD data. [ABSTRACT FROM AUTHOR]

Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory accuracy even further. Here, we proposed a deep-learning architecture model, namely Bidirectional long short-term memory with Channel and Spatial Attention network (BCSA), of which the training process is fully data-driven and end to end. It is based on data augmentation and SMILES tokenization technology without relying on auxiliary knowledge, such as complex spatial structure. In addition, our model takes the advantages of the long- and short-term memory network (LSTM) in sequence processing. The embedded channel and spatial attention modules in turn specifically identify the prime factors in the SMILES sequence for predicting properties. The model was further improved by Bayesian optimization. In this work, we demonstrate that the trained BSCA model is capable of predicting aqueous solubility. Furthermore, our proposed method shows noticeable superiorities and competitiveness in predicting oil–water partition coefficient, when compared with state-of-the-art graphs models, including graph convoluted network (GCN), message-passing neural network (MPNN), and AttentiveFP. [ABSTRACT FROM AUTHOR]

This study examined the behavior and penetration mechanisms of typical phenolic (benzoic) acids, which determine their observed penetration rates during membrane separation, focusing on the influence of electrostatic and hydrophobic solute/membrane interactions. To understand the effects of hydrophobicity and electrostatic interaction on membrane filtration, the observed penetration of five structurally similar phenolic acids was compared with regenerated cellulose (RC) and polyamide (PA) membranes at different solute concentrations and solution pHs. Variation partitioning analysis (VPA) was performed to calculate the relative contributions of electrostatic and hydrophobic effects. The penetration of phenolic acids was mainly influenced by the electrostatic interaction, with salicylic acid having the highest penetration. Penetration of phenolic acids through the PA membrane decreased from 98% at pH 3.0 to 30–50% at pH 7.4, indicating the dominance of the electrostatic interaction. Moreover, based on its hydrophobicity and greater surface charge, the PA membrane could separate binary mixtures of protocatechuic/salicylic acid and 4-hydroxybenzoic/salicylic acid at pH 9.0, with separation factors of 1.81 and 1.78, respectively. These results provide a greater understanding of solute/membrane interactions and their effect on the penetration of phenolic acids through polymeric ultrafiltration membranes.

Abstract Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water–octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of $$\hbox {Q}^{2}=0.973$$ Q2=0.973 and with the best performing nonconformity measure having median prediction interval of $$\pm ~0.39$$ ±0.39 log units at 80% confidence and $$\pm ~0.60$$ ±0.60 log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.

Abstract: The misuse of benzodiazepines as new psychoactive substances is an increasing problem around the world. Basic physicochemical and pharmacokinetic data is required on these substances to interpret and predict their effects upon humans. Experimental log D7.4, pKa and plasma protein binding values were determined for 11 benzodiazepines that have recently appeared as new psychoactive substances (3‐hydroxyphenazepam, 4′‐chlorodiazepam, desalkylflurazepam, deschloroetizolam, diclazepam, etizolam, flubromazepam, flubromazolam, meclonazepam, phenazepam, and pyrazolam) and compared with values generated by various software packages (ACD/I‐lab, MarvinSketch, ADMET Predictor and PreADMET). ACD/I‐LAB returned the most accurate values for log D7.4 and plasma protein binding while ADMET Predictor returned the most accurate values for pKa. Large variations in predictive errors were observed between compounds. Experimental values are currently preferable and desirable as they may aid with the future ‘training’ of predictive models for these new psychoactive substances. [ABSTRACT FROM AUTHOR]

• The sample pooling method has been validated and highlighted as an efficient approach for high-throughput screening of LogD/LogP, which significantly improves the experimental capacity by dramatically reducing the number of bioanalytical samples. • Theoretically elucidated the applicability of the sample pooling approach in the LogD/LogP determination. • The impact of DMSO content on LogD measurement was also investigated and the result demonstrated that at least 0.5% DMSO was tolerated in this method, which can guide the drafting of experimental protocols to ensure the good performance of the test. A new approach for screening LogD is presented. The method is based on the shake flask method combined with rapid generic LC-MS/MS bioanalysis by using a sample pooling approach that enables high-throughput screening of LogD or LogP in the drug discovery stage. The method is evaluated by a comparison of measured LogD between single and pooled compounds for a test set of structurally diverse compounds with a wide range of LogD values (from −0.04 to 6.01). Test compounds include 10 commercially available drug standards along with 27 new chemical entities. A good correlation (RMSE = 0.21, R2 = 0.9879) of LogD between the single and pooled compounds was obtained, suggesting that at least 37 compounds can be simultaneously measured with acceptable accuracy. The sample pooling method significantly reduced the number of bioanalysis samples as compared to the single compound measurement by the conventional shake flask method. The impact of DMSO content on LogD measurement was also investigated and the result demonstrated that at least 0.5% DMSO was tolerated in this method. The current new development will facilitate the drug discovery process by more rapidly assessing the LogD or LogP of drug candidates. [ABSTRACT FROM AUTHOR]

Abstract: Background: The measurement of homovanillic acid (HVA) and 3-methoxy-4-hydroxyphenylglycol (MHPG) in saliva is useful for diagnosis of some psychiatric disorders. Gum-chewing increases salivation and is sometimes used to collect an adequate sample volume of saliva within a shorter time. However, the rate of saliva production may affect the salivary concentrations of these catecholamine metabolites. We clarified whether the salivary concentrations of HVA and MHPG are dependent on the rate of salivation. Methods: Stimulated saliva was collected from healthy volunteers chewing a tasteless and flavorless chewing gum after unstimulated saliva was collected without gum-chewing. The salivary HVA and MHPG concentrations were measured using newly developed LC/ESI-MS/MS methods. Results: The salivary HVA and MHPG concentrations were reproducibly measured when saliva was collected without gum-chewing (unstimulated saliva). In contrast, their salivary concentrations significantly decreased when salivation was stimulated by gum-chewing (p <0.05, Wilcoxon signed-rank test). Conclusions: Stimulated saliva by gum-chewing should be used with caution in the measurement of the catecholamine metabolites. LogD is a useful indicator, when predicting whether the salivary concentration of a compound is dependent on the rate of salivation. [Copyright &y& Elsevier]

An efficient system for screening for the octanol/water distribution coefficient (logD) was developed. We previously developed a 96-well–based high-speed logD assay system (HSlogD) using a water-plug aspiration/injection method by auto liquid sampler (HTS-PAL) to analyze the water phase without contamination of the upper octanol phase. In this report, we introduce an automated liquid-handling system (Biomek2000) into the previously reported HSlogD system to dilute the octanol phase automatically. As a result, throughput is increased, allowing the analysis of 400 compounds per week with a range of 0

A common challenge for medicinal chemists is to reduce the pK a of strongly basic groups' conjugate acids into a range that preserves the desired effects, usually potency and/or solubility, but avoids undesired effects like high volume of distribution (V d), limited membrane permeation, and off-target binding to, notably, the hERG channel and monoamine receptors. We faced this challenge with a 3,4,5,6-tetrahydropyridine-2-amine scaffold harboring an amidine, a key structural component of potential inhibitors of BACE1, the rate-limiting enzyme in the production of Aβ species that make up amyloid plaques in Alzheimer's disease. In our endeavor to balance potency with desirable properties to achieve brain penetration, we introduced a diverse set of groups in beta position of the amidine that modulate logD, PSA and pK a. Given the synthetic challenge to prepare these highly functionalized warheads, we first developed a design flow including predicted physicochemical parameters which allowed us to select only the most promising candidates for synthesis. For this we evaluated a set of commercial packages to predict physicochemical properties, which can guide medicinal chemists in their endeavors to modulate pK a values of amidine and amine bases. [Display omitted] • Modulating pK a of highly basic centers is a common medicinal chemistry challenge, which we encountered with BACE1 inhibitors. • The effect of EWG on pK a , EPSA and CHI LogD of tetrahydropiperidine-2-amines is presented. • A 3-methylsulfonyl group emerged as most promising for its combined amidine pK a modulation and IMHB potential. • The performance of various predictive models for pK a , TPSA and LogP was compared versus experimental values. • All models excelled in predicting LogP, while none predicted EPSA accurately and QM models excelled in predicting pK a. UV metric titration with co-solvent. [75] For poorly soluble compounds, 5 μL of the abovementioned 10 mM compound stock solution and 25 μL mid-range phosphate buffer were titrated in 1.5 mL of a 80% methanol/20% ISA-water mixture (50% co-solvent). A titration was then carried out from pH 2 to pH 12. Secondly, the above mixture was further diluted with 0.15 mL ISA-water and titrated from pH 2 to pH 12 (40% co-solvent). Thirdly, the above mixture was further diluted with 0.35 mL ISA-water and titrated from pH 2 to pH 12 (30% co-solvent). From these three titrations pK a was calculated via Yasuda-Shedlovsky extrapolation. [ABSTRACT FROM AUTHOR]

A new addition method is described in this study for calculating the partition coefficients of peptides. Log P and log D values of peptides are calculated by summing the contributions of the component amino acids. The final models are derived from a multivariate linear regression analysis of 219 peptides with known experimental data. The standard errors in a leave-one-out cross-validation are 0.23 and 0.24 log units for the log P and log D values, respectively. The predictive ability of the model is tested by an extra set of ten peptides, and the self-consistency of the model is further demonstrated by a new validation procedure called the evolution test. The parameters obtained in regression could be used as hydrophobicity scales for amino acids. The application of such hydrophobicity scales has also been discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] Nicotinamide phosphoribosyltransferase (NAMPT) catalyzes the rate-limiting step of the NAD+ salvage pathway. Since NAD+ plays a pivotal role in many biological processes including metabolism and aging, activation of NAMPT is an attractive therapeutic target for treatment of diverse array of diseases. Herein, we report the continued optimization of novel urea-containing derivatives which were identified as potent NAMPT activators. Early optimization of HTS hits afforded compound 12 , with a triazolopyridine core, as a lead compound. CYP direct inhibition (DI) was identified as an issue of concern, and was resolved through modulation of lipophilicity to culminate in 1-[2-(1-methyl-1H-pyrazol-5-yl)-[1,2,4]triazolo[1,5- a ]pyridin-6-yl]-3-(pyridin-4-ylmethyl)urea (21), which showed potent NAMPT activity accompanied with attenuated CYP DI towards multiple CYP isoforms. [ABSTRACT FROM AUTHOR]

The misuse of benzodiazepines as new psychoactive substances is an increasing problem around the world. Basic physicochemical and pharmacokinetic data is required on these substances to interpret and predict their effects upon humans. Experimental log D 7.4 , pK a and plasma protein binding values were determined for 11 benzodiazepines that have recently appeared as new psychoactive substances (3-hydroxyphenazepam, 4'-chlorodiazepam, desalkylflurazepam, deschloroetizolam, diclazepam, etizolam, flubromazepam, flubromazolam, meclonazepam, phenazepam, and pyrazolam) and compared with values generated by various software packages (ACD/I-lab, MarvinSketch, ADMET Predictor and PreADMET). ACD/I-LAB returned the most accurate values for log D 7.4 and plasma protein binding while ADMET Predictor returned the most accurate values for pK a . Large variations in predictive errors were observed between compounds. Experimental values are currently preferable and desirable as they may aid with the future 'training' of predictive models for these new psychoactive substances., (Copyright © 2018 John Wiley & Sons, Ltd.)

The unbound drug concentration-effect relationship in brain is a key aspect in CNS drug discovery and development. In this work, we describe an in vitro high-throughput distribution assay between an aqueous buffer and a microemulsion of porcine brain polar lipids (BPL). The derived distribution coefficient LogDBPL was applied to the prediction of unbound drug concentrations in brain (Cu,b) and nonspecific binding to brain tissue. The in vivo relevance of the new assay was assessed for a large set of proprietary drug candidates and CNS drugs by (1) comparing observed compound concentrations in rat CSF with Cu,b calculated using the LogDBPL assay in combination with total drug brain concentrations, (2) comparing Cu,b derived from LogDBPL and total drug brain concentrations to Cu,b estimated using in vitro P-glycoprotein efflux ratio data and unbound drug plasma levels, and (3) comparing tissue nonspecific binding data from human brain autoradiography studies for 17 PET tracer candidates to distribution in BPL. In summary, the LogDBPL assay provides a predicted drug fraction unbound in brain tissue that is nearly identical to brain homogenate equilibrium dialysis with an estimation of in vivo Cu,b that is superior to LogD in octanol. LogDBPL complements the approach for predicting Cu,b based on in vitro P-glycoprotein efflux ratio and in vivo unbound plasma concentration and stands as a fast and cost-effective tool for nonspecific brain binding optimization of PET ligand candidates.

Several procedures based on the shake-flask method and designed to require a minimum amount of drug for octanol-water partition coefficient determination have been established and developed. The procedures have been validated by a 28 substance set with a lipophilicity range from -2.0 to 4.5 (logD7.4). The experimental partition is carried out using aqueous phases buffered with phosphate (pH 7.4) and n-octanol saturated with buffered water and the analysis is performed by liquid chromatography. In order to have accurate results, four procedures and eight different ratios between phase volumes are proposed. Each procedure has been designed and optimized (for partition ratios) for a specific range of drug lipophilicity (low, regular and high lipophilicity) and solubility (high and low aqueous solubility). The procedures have been developed to minimize the measurement in the octanolic phase. Experimental logD7.4 values obtained from different procedures and partition ratios show a standard deviation lower than 0.3 and there is a nice agreement when these values are compared with the reference literature ones., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Incorporation of nitrogen is a common medicinal chemistry tactic to reduce logD values. Neighboring group participation influences logD, so the results are isomer dependent. The logD and logP differences observed between isomeric pyrimidines 1, 2 and 3 presumably result when the carbonyl or ether lone pairs are in close proximity to a heterocyclic nitrogen lone pair, recruiting water to bridge between the electron rich atoms. Various lipophilicity calculators did not discriminate between 1 (logD=2.6) and 3 (logD=1.0), but solvation energies using Poisson-Boltzmann and 3D-RISM methods rationalize the observed differences in lipophilicity among pyrimidine carboxamide isomers., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

The degradation of 30 trace organic contaminants (TrOC) by a white-rot fungus-augmented membrane bioreactor (MBR) was investigated. The results show that white-rot fungal enzyme (laccase), coupled with a redox mediator (1-hydroxy benzotriazole, HBT), could degrade TrOC that are resistant to bacterial degradation (e.g. diclofenac, triclosan, naproxen and atrazine) but achieved low removal of compounds (e.g. ibuprofen, gemfibrozil and amitriptyline) that are well removed by conventional activated sludge treatment. Overall, the fungus-augmented MBR showed better TrOC removal compared to a system containing conventional activated sludge. The major role of biodegradation in removal by the MBR was noted. Continuous mediator dosing to MBR may potentially enhance its performance, although not as effectively as for mediator-enhanced batch laccase systems. A ToxScreen3 assay revealed no significant increase in the toxicity of the effluent during MBR treatment of the synthetic wastewater comprising TrOC, confirming that no toxic by-products were produced., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

A QSAR model for predicting passive permeability (Papp ) was derived from Papp values measured in the LLC-PK1 cell line. The QSAR method and descriptor set that performed best in terms of cross-validation was random forest with a combination of AP, DP, and MOE&#95;2D descriptors. The QSAR model was used to predict the Caco-2 cell permeability for 313 compounds described in the literature with good success. We find that passive permeability for different cell lines can be predicted with similar molecular properties and descriptors. It is shown that the variation in experimental measurements of Papp is smaller than the error in QSAR predictions indicating that predictions are not quantitatively perfect, although qualitatively useful. We get better predictions if the training set is large and diverse, rather than smaller and more internally consistent. This is because prediction accuracy falls off quickly with decreasing similarity to the training set and it is therefore better to have as large a training set as possible. While single physical parameters are not as good as a full QSAR model in predicting Papp , logD seems the most important parameter. Intermediate values of logD are associated with higher Papp ., (Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)