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Exhaustive quantitative structure-property relationship (QSPR) modeling of the separation factor logSF for 46 polyazaheterocyclic ligands extracting Am3+ and Eu3+ from nitric acid aqueous solution to the 1,1,2,2–tetrachloroethane phase has been done using different computational approaches. Modeling methods included Multiple Linear Regression, Radial Basis Function Neural Networks, and Associated Neural Networks; two types of descriptors (substructural molecular fragments and molecular descriptors) and different techniques of variable selection have been employed. The developed QSPR models applied for novel t-Bu-hemi-BTP ligand resulted in logSF=1.07-1.46; these predicted values somewhat exceed the experimental value logSF=1.0. Several hypothetical extractants potentially possessing high logSF values are proposed. An influence of uncertainties in initial experimental data as well as the choice of the theoretical approach on the performance of QSPR models is discussed. [ABSTRACT FROM AUTHOR]

Differential scanning fluorimetry (DSF) is a technique that reports protein thermal stability via the selective recognition of unfolded states by fluorogenic dyes. However, DSF applications remain limited by protein incompatibilities with existing DSF dyes. Here we overcome this obstacle with the development of a protein-adaptive DSF platform (paDSF) that combines a dye library 'Aurora' with a streamlined procedure to identify protein-dye pairs on demand. paDSF was successfully applied to 94% (66 of 70) of proteins, tripling the previous compatibility and delivering assays for 66 functionally and biochemically diverse proteins, including 10 from severe acute respiratory syndrome coronavirus 2. We find that paDSF can be used to monitor biological processes that were previously inaccessible, demonstrated for the interdomain allostery of O-GlcNAc transferase. The chemical diversity and varied selectivities of Aurora dyes suggest that paDSF functionality may be readily extended. paDSF is a generalizable tool to interrogate protein stability, dynamics and ligand binding., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.

Drug-resistant bacteria are a worldwide public health concern. As the prevalence of multi-drug resistant pathogens outpaces the discovery of new antibacterials, it is of importance to explore the structure-activity relationships for series of known bactericides with proven scaffolds. Herein, we assembled a set of 507 fluoroquinolone analogues all experimentally tested for their inhibition potency against four pathogens: Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae. We relied on cheminformatics techniques to characterize and cluster them based on their structural similarity and analyzed the structure-activity relationships identified for each cluster of fluoroquinolones. Then, we utilized machine learning techniques to develop and validate predictive QSAR models for computing the inhibition potencies (pMIC) of analogues for each pathogen. These QSAR models afforded reasonable external prediction performances (R2≥0.6, MAE∼0.4). This study confirmed that (i) there are both global and local inter-pathogen concordance regarding the antibacterial potency of fluoroquinolones, (ii) small clusters of fluoroquinolone analogues are characterized by unique patterns of strain selectivity and potency, the latter being potentially useful to design new analogues with enhanced potency and/or selectivity towards a given pathogen, and (iii) robust QSAR models were obtained allowing for future design of new bioactive fluoroquinolones., (© 2020 Wiley-VCH GmbH.)

Simplified molecular input line entry system (SMILES)-based deep learning models are slowly emerging as an important research topic in cheminformatics. In this study, we introduce SMILES pair encoding (SPE), a data-driven tokenization algorithm. SPE first learns a vocabulary of high-frequency SMILES substrings from a large chemical dataset (e.g., ChEMBL) and then tokenizes SMILES based on the learned vocabulary for the actual training of deep learning models. SPE augments the widely used atom-level tokenization by adding human-readable and chemically explainable SMILES substrings as tokens. Case studies show that SPE can achieve superior performances on both molecular generation and quantitative structure-activity relationship (QSAR) prediction tasks. In particular, the SPE-based generative models outperformed the atom-level tokenization model in the aspects of novelty, diversity, and ability to resemble the training set distribution. The performance of SPE-based QSAR prediction models were evaluated using 24 benchmark datasets where SPE consistently either did match or outperform atom-level and k -mer tokenization. Therefore, SPE could be a promising tokenization method for SMILES-based deep learning models. An open-source Python package SmilesPE was developed to implement this algorithm and is now freely available at https://github.com/XinhaoLi74/SmilesPE.

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals., Objectives: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 ( LD 50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [ LD 50 ( LD 50 ≤ 50 mg / kg )], and nontoxic chemicals ( L D 50 > 2,000 mg / kg )., Methods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches., Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results., Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Well-known 4-hydroxycoumarin derivatives, such as warfarin, act as inhibitors of the vitamin K epoxide reductase (VKOR) and are used as anticoagulants. Mutations of the VKOR enzyme can lead to resistance to those compounds. This has been a problem in using them as medicine or rodenticide. Most of these mutations lie in the vicinity of potential warfarin-binding sites within the ER-luminal loop structure (Lys30, Phe55) and the transmembrane helix (Tyr138). However, a VKOR mutation found in Tokyo in warfarin-resistant rats does not follow that pattern (Leu76Pro), and its effect on VKOR function and structure remains unclear. We conducted both in vitro kinetic analyses and in silico docking studies to characterize the VKOR mutant. On the one hand, resistant rats (R-rats) showed a 37.5-fold increased IC 50 value to warfarin when compared to susceptible rats (S-rats); on the other hand, R-rats showed a 16.5-fold lower basal VKOR activity (V max /K m ). Docking calculations exhibited that the mutated VKOR of R-rats has a decreased affinity for warfarin. Molecular dynamics simulations further revealed that VKOR-associated warfarin was more exposed to solvents in R-rats and key interactions between Lys30, Phe55, and warfarin were less favored. This study concludes that a single mutation of VKOR at position 76 leads to a significant resistance to warfarin by modifying the types and numbers of intermolecular interactions between the two., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Humans are concurrently exposed to chemically, structurally and toxicologically diverse chemicals. A critical challenge for environmental epidemiology is to quantify the risk of adverse health outcomes resulting from exposures to such chemical mixtures and to identify which mixture constituents may be driving etiologic associations. A variety of statistical methods have been proposed to address these critical research questions. However, they generally rely solely on measured exposure and health data available within a specific study. Advancements in understanding of the role of mixtures on human health impacts may be better achieved through the utilization of external data and knowledge from multiple disciplines with innovative statistical tools. In this paper we develop new methods for health analyses that incorporate auxiliary information about the chemicals in a mixture, such as physicochemical, structural and/or toxicological data. We expect that the constituents identified using auxiliary information will be more biologically meaningful than those identified by methods that solely utilize observed correlations between measured exposure. We develop flexible Bayesian models by specifying prior distributions for the exposures and their effects that include auxiliary information and examine this idea over a spectrum of analyses from regression to factor analysis. The methods are applied to study the effects of volatile organic compounds on emergency room visits in Atlanta. We find that including cheminformatic information about the exposure variables improves prediction and provides a more interpretable model for emergency room visits for respiratory diseases.

To fully enable the development of diagnostic tools and progressive pharmaceutical drugs, it is imperative to understand the molecular changes occurring before and during disease onset and progression. Systems biology assessments utilizing multi-omic analyses (e.g. the combination of proteomics, lipidomics, genomics, etc.) have shown enormous value in determining molecules prevalent in diseases and their associated mechanisms. Herein, we utilized multi-omic evaluations, multi-dimensional analysis methods, and new cheminformatics-based visualization tools to provide an in depth understanding of the molecular changes taking place in preeclampsia (PRE) and gestational diabetes mellitus (GDM) patients. Since PRE and GDM are two prevalent pregnancy complications that result in adverse health effects for both the mother and fetus during pregnancy and later in life, a better understanding of each is essential. The multi-omic evaluations performed here provide new insight into the end-stage molecular profiles of each disease, thereby supplying information potentially crucial for earlier diagnosis and treatments.

Since its inception, the main goal of the lipidomics field has been to characterize lipid species and their respective biological roles. However, difficulties in both full speciation and biological interpretation have rendered these objectives extremely challenging and as a result, limited our understanding of lipid mechanisms and dysregulation. While mass spectrometry-based advancements have significantly increased the ability to identify lipid species, less progress has been made surrounding biological interpretations. We have therefore developed a Structural-based Connectivity and Omic Phenotype Evaluations (SCOPE) cheminformatics toolbox to aid in these evaluations. SCOPE enables the assessment and visualization of two main lipidomic associations: structure/biological connections and metadata linkages either separately or in tandem. To assess structure and biological relationships, SCOPE utilizes key lipid structural moieties such as head group and fatty acyl composition and links them to their respective biological relationships through hierarchical clustering and grouped heatmaps. Metadata arising from phenotypic and environmental factors such as age and diet is then correlated with the lipid structures and/or biological relationships, utilizing Toxicological Prioritization Index (ToxPi) software. Here, SCOPE is demonstrated for various applications from environmental studies to clinical assessments to showcase new biological connections not previously observed with other techniques.

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) pose a substantial threat as endocrine disruptors, and thus early identification of those that may interact with steroid hormone receptors, such as the androgen receptor (AR), is critical. In this study we screened 5,206 PFASs from the CompTox database against the different binding sites on the AR using both molecular docking and machine learning techniques. We developed support vector machine models trained on Tox21 data to classify the active and inactive PFASs for AR using different chemical fingerprints as features. The maximum accuracy was 95.01% and Matthew's correlation coefficient (MCC) was 0.76 respectively, based on MACCS fingerprints (MACCSFP). The combination of docking-based screening and machine learning models identified 29 PFASs that have strong potential for activity against the AR and should be considered priority chemicals for biological toxicity testing., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The impact of next-generation biorecognition elements (ligands) will be determined by the ability to remotely control their binding activity for a target biomolecule in complex environments. Compared to conventional mechanisms for regulating binding affinity (pH, ionic strength, or chaotropic agents), light provides higher accuracy and rapidity, and is particularly suited for labile targets. In this study, we demonstrate a general method to develop azobenzene-cyclized peptide ligands with light-controlled affinity for target proteins. Light triggers a cis/trans isomerization of the azobenzene, which results in a major structural rearrangement of the cyclic peptide from a non-binding to a binding configuration. Critical to this goal are the ability to achieve efficient photo-isomerization under low light dosage and the temporal stability of both cis and trans isomers. We demonstrated our method by designing photo-switchable peptides targeting vascular cell adhesion marker 1 (VCAM1), a cell marker implicated in stem cell function. Starting from a known VCAM1-binding linear peptide, an ensemble of azobenzene-cyclized variants with selective light-controlled binding were identified by combining in silico design with experimental characterization via spectroscopy and surface plasmon resonance. Variant cycloAZOB[G-VHAKQHRN-K] featured rapid, light-controlled binding of VCAM1 (KD,trans/KD,cis ∼ 130). Biotin-cycloAZOB[G-VHAKQHRN-K] was utilized to label brain microvascular endothelial cells (BMECs), showing co-localization with anti-VCAM1 antibodies in cis configuration and negligible binding in trans configuration.

The quest for ligands alternative to Protein A for the purification of monoclonal antibodies (mAbs) has been pursued for almost three decades. Yet, the IgG-binding peptides known to date still fall short of the host cell protein (HCP) logarithmic removal value (LRV) set by Protein A media (2.5-3.1). In this study, we present an integrated computational-experimental approach leading to the discovery of peptide ligands that provide HCP LRVs on par with Protein A. First, the screening of 60,000 peptide variants was performed using a high-throughput search algorithm to identify sequences that ensure IgG affinity binding. Select sequences WQRHGI, MWRGWQ, RHLGWF, and GWLHQR were then negatively screened in silico against a panel of model HCPs to ensure the selection of peptides with high binding selectivity. Candidate ligands WQRHGI and MWRGWQ were conjugated to chromatographic resins and characterized by isothermal binding and breakthrough assays to quantify static and dynamic binding capacity (Q max and DBC 10% ), respectively. The resulting Q max were 52.6 mg of IgG per mL of adsorbent for WQRHGI and 57.48 mg/mL for MWRGWQ, while the DBC 10% (2 minutes residence time) were 30.1 mg/mL for WQRHGI and 36.4 mg/mL for MWRGWQ. Evaluation of the peptides by isothermal titration calorimetry (ITC) confirmed the binding energy predicted in silico, and an amino acid scanning study corroborated the affinity-like binding activity of the peptides. WQRHGI-WorkBeads resin was finally characterized by purification of a monoclonal antibody from a Chinese Hamster Ovary (CHO) cell culture harvest, affording a remarkable HCP LRV of 2.7, and consistent product yield and purity over 100 chromatographic cycles. These results demonstrate the potential of WQRHGI as an effective alternative to Protein A for antibody purification., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020. Published by Elsevier B.V.)

Imatinib, a 2-phenylaminopyridine-based BCR-ABL tyrosine kinase inhibitor, is a highly effective drug for treating Chronic Myeloid Leukemia (CML). However, cases of drug resistance are constantly emerging due to various mutations in the ABL kinase domain; thus, it is crucial to identify novel bioactive analogues. Reliable QSAR models and molecular docking protocols have been shown to facilitate the discovery of new compounds from chemical libraries prior to experimental testing. However, as the vast majority of QSAR models strictly relies on 2D descriptors, the rise of 3D descriptors directly computed from molecular dynamics simulations offers new opportunities to potentially augment the reliability of QSAR models. Herein, we employed molecular docking and molecular dynamics on a large series of Imatinib derivatives and developed an ensemble of QSAR models relying on deep neural nets (DNN) and hybrid sets of 2D/3D/MD descriptors in order to predict the binding affinity and inhibition potencies of those compounds. Through rigorous validation tests, we showed that our DNN regression models achieved excellent external prediction performances for the pKi data set (n = 555, R 2 ≥ 0.71. and MAE ≤ 0.85), and the pIC 50 data set (n = 306, R 2 ≥ 0.54. and MAE ≤ 0.71) with strict validation protocols based on external test sets and 10-fold native and nested cross validations. Interestingly, the best DNN and random forest models performed similarly across all descriptor sets. In fact, for this particular series of compounds, our external test results suggest that incorporating additional 3D protein-ligand binding site fingerprint, descriptors, or even MD time-series descriptors did not significantly improve the overall R 2 but lowered the MAE of DNN QSAR models. Those augmented models could still help in identifying and understanding the key dynamic protein-ligand interactions to be optimized for further molecular design.

Pneumonia, of which Streptococcus pneumoniae is the most common causative agent, is considered one of the three top leading causes of death worldwide. As seen in other bacterial species, antimicrobial resistance is on the rise for this pathogen. Therefore, there is a pressing need for novel antimicrobial strategies to combat these infections. Recently, uridine diphosphate glucose pyrophosphorylase (UDPG:PP) has been put forward as a potential drug target worth investigating. Moreover, earlier research demonstrated that streptococci lacking a functional galU gene (encoding for UDPG:PP) were characterized by significantly reduced in vitro and in vivo virulence. Therefore, in this study we evaluated the anti-virulence activity of potential UDPG:PP inhibitors. They were selected in silico using a tailor-made streptococcal homology model, based on earlier listerial research. While the compounds didn't affect bacterial growth, nor affected in vitro adhesion to and phagocytosis in macrophages, the amount of polysaccharide capsule was significantly reduced after co-incubation with these inhibitors. Moreover, co-incubation proved to have a positive effect on survival in an in vivo Galleria mellonella larval infection model. Therefore, rather than targeting bacterial survival directly, these compounds proved to have an effect on streptococcal virulence by lowering the amount of polysaccharide and thereby probably boosting recognition of this pathogen by the innate immune system. While the compounds need adaptation to broaden their activity to more streptococcal strains rather than being strain-specific, this study consolidates UDPG:PP as a potential novel drug target., (Copyright © 2020 Cools, Triki, Geerts, Delputte, Fourches and Cos.)

Correction for 'QSAR without borders' by Eugene N. Muratov et al., Chem. Soc. Rev., 2020, DOI: 10.1039/d0cs00098a.

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

Deep neural networks can directly learn from chemical structures without extensive, user-driven selection of descriptors in order to predict molecular properties/activities with high reliability. But these approaches typically require large training sets to learn the endpoint-specific structural features and ensure reasonable prediction accuracy. Even though large datasets are becoming the new normal in drug discovery, especially when it comes to high-throughput screening or metabolomics datasets, one should also consider smaller datasets with challenging endpoints to model and forecast. Thus, it would be highly relevant to better utilize the tremendous compendium of unlabeled compounds from publicly-available datasets for improving the model performances for the user's particular series of compounds. In this study, we propose the Molecular Prediction Model Fine-Tuning (MolPMoFiT) approach, an effective transfer learning method based on self-supervised pre-training + task-specific fine-tuning for QSPR/QSAR modeling. A large-scale molecular structure prediction model is pre-trained using one million unlabeled molecules from ChEMBL in a self-supervised learning manner, and can then be fine-tuned on various QSPR/QSAR tasks for smaller chemical datasets with specific endpoints. Herein, the method is evaluated on four benchmark datasets (lipophilicity, FreeSolv, HIV, and blood-brain barrier penetration). The results showed the method can achieve strong performances for all four datasets compared to other state-of-the-art machine learning modeling techniques reported in the literature so far.

We report on a new cheminformatics enumeration technology-SIME, synthetic insight-based macrolide enumerator-a new and improved software technology. SIME can enumerate fully assembled macrolides with synthetic feasibility by utilizing the constitutional and structural knowledge extracted from biosynthetic aspects of macrolides. Taken into account by the software are key information such as positions in macrolide structures at which chemical components can be inserted, and the types of structural motifs and sugars of interest that can be synthesized and incorporated at those positions. Additionally, we report on the chemical distribution analysis of the newly SIME-generated V1B (virtual 1 billion) library of macrolides. Those compounds were built based on the core of the Erythromycin structure, 13 structural motifs and a library of sugars derived from eighteen bioactive macrolides. This new enumeration technology can be coupled with cheminformatics approaches such as QSAR modeling and molecular docking to aid in drug discovery for rational designing of next generation macrolide therapeutics with desirable pharmacokinetic properties.

Reliable in silico approaches to replace animal testing for the evaluation of potential acute toxic effects are highly demanded by regulatory agencies. In particular, quantitative structure-activity relationship (QSAR) models have been used to rapidly assess chemical induced toxicity using either continuous ( regression ) or discrete ( classification ) predictions. However, it is often unclear how those different types of models can complement and potentially help each other to afford the best prediction accuracy for a given chemical. This paper presents a novel, dual-layer hierarchical modeling method to fully integrate regression and classification QSAR models for assessing rat acute oral systemic toxicity, with respect to regulatory classifications of concern. The first layer of independent regression, binary, and multiclass models ( base models) were solely built using computed chemical descriptors/fingerprints. Then, a second layer of models ( hierarchical models) were built by stacking all the cross-validated out-of-fold predictions from the base models. All models were validated using an external test set, and we found that the hierarchical models did outperform the base models for all three end points. The hierarchical quantitative structure-activity relationship (H-QSAR) modeling method represents a promising approach for chemical toxicity prediction and more generally for stacking and blending individual QSAR models into more predictive ensemble models.

Background: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling., Objectives: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP)., Methods: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays., Results: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set., Discussion: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼ 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

Introduction : Predictive Quantitative Structure-Activity Relationship (QSAR) modeling has become an essential methodology for rapidly assessing various properties of chemicals. The vast majority of these QSAR models utilize numerical descriptors derived from the two- and/or three-dimensional structures of molecules. However, the conformation-dependent characteristics of flexible molecules and their dynamic interactions with biological target(s) is/are not encoded by these descriptors, leading to limited prediction performances and reduced interpretability. 2D/3D QSAR models are successful for virtual screening, but typically suffer at lead optimization stages. That is why conformation-dependent 4D-QSAR modeling methods were developed two decades ago. However, these methods have always suffered from the associated computational cost. Recently, 4D-QSAR has been experiencing a significant come-back due to rapid advances in GPU-accelerated molecular dynamic simulations and modern machine learning techniques. Areas covered : Herein, the authors briefly review the literature regarding 4D-QSAR modeling and describe its modern workflow called MD-QSAR. Challenges and current limitations are also highlighted. Expert opinion : The development of hyper-predictive MD-QSAR models could represent a disruptive technology for analyzing, understanding, and optimizing dynamic protein-ligand interactions with countless applications for drug discovery and chemical toxicity assessment. Therefore, there has never been a better time and relevance for molecular modeling teams to engage in hyper-predictive MD-QSAR modeling.