Do Structurally Similar Ligands Bind in a Similar Fashion?

Adaptive Space Search-based Molecular Evolution Optimization Algorithm

MOTIVATION

In drug development process, a significant portion of budget and research time are dedicated to the lead compound optimization procedure in order to identify potential drugs. This procedure focuses on enhancing the pharmacological and bioactive properties of compounds by optimizing their local substructures. However, due to the vast and discrete chemical structure space and the unpredictable element combinations within this space, the optimization process is inherently complex. Various structure enumeration-based combinatorial optimization methods have shown certain advantages. However, they still have limitations. Those methods fail to consider the differences between molecules and struggle to explore the unknown outer search space.

RESULTS

In this study, we propose an adaptive space search-based molecular evolution optimization algorithm (ASSMOEA). It consists of three key modules: construction of molecule-specific search space, molecular evolutionary optimization, and adaptive expansion of molecule-specific search space. Specifically, we design a fragment similarity tree in molecule-specific search space, and apply a dynamic mutation strategy in this space to guide molecular optimization. Then we utilize an encoder-encoder structure to adaptively expand the space. Those three modules are circled iteratively to optimize molecules. Our experiments demonstrate that ASSMOEA outperforms existing methods in terms of molecular optimization. It not only enhances the efficiency of the molecular optimization process, but also exhibits a robust ability to search for correct solutions.

AVAILABILITY AND IMPLEMENTATION

The code is freely available on the web at https://github.com/bbbbb-b/MEOAFST.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy

Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.

A High-Quality Data Set of Protein-Ligand Binding Interactions Via Comparative Complex Structure Modeling

High-quality protein-ligand complex structures provide the basis for understanding the nature of noncovalent binding interactions at the atomic level and enable structure-based drug design. However, experimentally determined complex structures are scarce compared with the vast chemical space. In this study, we addressed this issue by constructing the BindingNet data set via comparative complex structure modeling, which contains 69,816 modeled high-quality protein-ligand complex structures with experimental binding affinity data. BindingNet provides valuable insights into investigating protein-ligand interactions, allowing visual inspection and interpretation of structural analogues' structure-activity relationships. It can also be used for evaluating machine-learning-based scoring functions. Our results indicate that machine learning models trained on BindingNet could reduce the bias caused by buried solvent-accessible surface area, as we previously found for models trained on the PDBbind data set. We also discussed strategies to improve BindingNet and its potential utilization for benchmarking the molecular docking methods and ligand binding free energy calculation approaches. The BindingNet complements PDBbind in constructing a sufficient and unbiased protein-ligand binding data set and is freely available at http://bindingnet.huanglab.org.cn.

Herbicide Safeners: From Molecular Structure Design to Safener Activity

Herbicide safeners, highly effective antidotes, find widespread application in fields for alleviating the phytotoxicity of herbicides to crops. Designing new herbicide safeners remains a notable issue in pesticide research. This review focuses on discussing and summarizing the structure-activity relationships, molecular structures, physicochemical properties, and molecular docking of herbicide safeners in order to explore how different structures affect the safener activities of target compounds. It also provides insights into the application prospects of computer-aided drug design for designing and synthesizing new safeners in the future.

Secondary metabolites of Trichoderma spp. as EGFR tyrosine kinase inhibitors: Evaluation of anticancer efficacy through computational approach

The present study explores the epidermal growth factor receptor (EGFR) tyrosine kinase inhibition efficacy of secondary metabolites in Trichoderma spp. through molecular docking, molecular dynamics (MD) simulation and MM-PBSA approach. The result of molecular docking confirmed that out of 200 metabolites screened, three metabolites such as Harzianelactone A, Pretrichodermamide G and Aspochalasin M, potentially bound with the active binding site of EGFR tyrosine kinase domain(PDB ID: 1M17) with a threshold docking score of ≤- 9.0 kcal/mol when compared with the standard EGFR inhibitor (Erlotinib). The MD simulation was run to investigate the potential for stable complex formation in EGFR tyrosine kinase domain-unbound/lead metabolite (Aspochalasin M)-bound/standard inhibitor (Erlotinib)-bound complex. The MD simulation analysis at 100 ns revealed that Aspochalasin M formed the stable complex with EGFR. Besides, the in silico predication of pharmacokinetic properties further confirmed that Aspochalasin M qualified the drug-likeness rules with no harmful side effects (viz., hERG toxicity, hepatotoxicity and skin sensitization), non-mutagenicity and favourable logBB value. Moreover, the BOILED-Egg model predicted that Aspochalasin M showed a higher gastrointestinal absorption with improved bioavailability when administered orally and removed from the central nervous system (CNS). The results of the computational studies concluded that Aspochalasin M possessed significant efficacy in binding EGFR's active sites compared to the known standard inhibitor (Erlotinib). Therefore, Aspochalasin M can be used as a possible anticancer drug candidate and further in vitro and in vivo experimental validation of Aspochalasin M of Trichoderma spp. are required to determine its anticancer potential.

Integrating Reaction Schemes, Reagent Databases, and Virtual Libraries into Fragment-Based Design by Reinforcement Learning

Lead optimization supported by artificial intelligence (AI)-based generative models has become increasingly important in drug design. Success factors are reagent availability, novelty, and the optimization of multiple properties. Directed fragment-replacement is particularly attractive, as it mimics medicinal chemistry tactics. Here, we present variations of fragment-based reinforcement learning using an actor-critic model. Novel features include freezing fragments and using reagents as the fragment source. Splitting molecules according to reaction schemes improves synthesizability, while tuning network output probabilities allows us to balance novelty versus diversity. Combining fragment-based optimization with virtual library encodings allows the exploration of large chemical spaces with synthesizable ideas. Collectively, these enhancements influence design toward high-quality molecules with favorable profiles. A validation study using 15 pharmaceutically relevant targets reveals that novel structures are obtained for most cases, which are identical or related to independent validation sets for each target. Hence, these modifications significantly increase the value of fragment-based reinforcement learning for drug design. The code is available on GitHub: https://github.com/Sanofi-Public/IDD-papers-fragrl.

Chemical similarity of molecules with physiological response

Measuring the similarity among molecules is an important task in various chemically oriented problems. This elusive concept is hard to define and quantify. Moreover, the complexity of the problem is elevated by bifurcating the notion of molecular similarity to structural and chemical similarity. While the structural similarity of molecules is being extensively researched, the so-called chemical similarity is being mentioned scarcely. Here, we propose a way of converting the physico-chemical properties into molecular fingerprints. Then, using the apparatus of measuring the structural similarity, the chemical similarity can be assessed. The proof of a concept is demonstrated on a set of molecules that induce diverse physiological responses.

Synthesis and Receptor Binding Studies of α5 GABAAR Selective Novel Imidazodiazepines Targeted for Psychiatric and Cognitive Disorders

GABA mediates inhibitory actions through various GABA_A receptor subtypes, including 19 subunits in human GABAAR. Dysregulation of GABAergic neurotransmission is associated with several psychiatric disorders, including depression, anxiety, and schizophrenia. Selective targeting of α2/3 GABAARs can treat mood and anxiety, while α5 GABAA-Rs can treat anxiety, depression, and cognitive performance. GL-II-73 and MP-III-022, α5-positive allosteric modulators have shown promising results in animal models of chronic stress, aging, and cognitive disorders, including MDD, schizophrenia, autism, and Alzheimer's disease. Described in this article is how small changes in the structure of imidazodiazepine substituents can greatly impact the subtype selectivity of benzodiazepine GABAAR. To investigate alternate and potentially more effective therapeutic compounds, modifications were made to the structure of imidazodiazepine 1 to synthesize different amide analogs. The novel ligands were screened at the NIMH PDSP against a panel of 47 receptors, ion channels, including hERG, and transporters to identify on- and off-target interactions. Any ligands with significant inhibition in primary binding were subjected to secondary binding assays to determine their K_i values. The newly synthesized imidazodiazepines were found to have variable affinities for the benzodiazepine site and negligible or no binding to any off-target profile receptors that could cause other physiological problems.

An Analysis of Successful Hit-to-Clinical Candidate Pairs

An analysis of 156 published clinical candidates from the Journal of Medicinal Chemistry between 2018 and 2021 was conducted to identify lead generation strategies most frequently employed leading to drug candidates. As in a previous publication, the most frequent lead generation strategies resulting in clinical candidates were from known compounds (59%) followed by random screening approaches (21%). The remainder of the approaches included directed screening, fragment screening, DNA-encoded library screening (DEL), and virtual screening. An analysis of similarity was also conducted based on Tanimoto-MCS and revealed most clinical candidates were distant from their original hits; however, most shared a key pharmacophore that translated from hit-to-clinical candidate. An examination of frequency of oxygen, nitrogen, fluorine, chlorine, and sulfur incorporation in clinical candidates was also conducted. The three most similar and least similar hit-to-clinical pairs from random screening were examined to provide perspective on changes that occur that lead to successful clinical candidates.

Computational design, synthesis and biological evaluation of PDE5 inhibitors based on N2,N4-diaminoquinazoline and N2,N6-diaminopurine scaffolds

We report the synthesis, and characterization of twenty-nine new inhibitors of PDE5. Structure-based design was employed to modify to our previously reported 2,4-diaminoquinazoline series. Modification include scaffold hopping to 2,6-diaminopurine core as well as incorporation of ionizable groups to improve both activity and solubility. The prospective binding mode of the compounds was determined using 3D ligand-based similarity methods to inhibitors of known binding mode, combined with a PDE5 docking and molecular dynamics based-protocol, each of which pointed to the same binding mode. Chemical modifications were then designed to both increase potency and solubility as well as validate the binding mode prediction. Compounds containing a quinazoline core displayed IC₅₀s ranging from 0.10 to 9.39 µM while those consisting of a purine scaffold ranging from 0.29 to 43.16 µM. We identified 25 with a PDE5 IC₅₀ of 0.15 µM, and much improved solubility (1.77 mg/mL) over the starting lead. Furthermore, it was found that the predicted binding mode was consistent with the observed SAR validating our computationally driven approach.

Diazabicyclo derivatives as safeners protect cotton from injury caused by flumioxazin

Flumioxazin, a protoporphyrinogen oxidase (PPO; EC 1.3.3.4) inhibitor, has been used in soybean, cotton, grapes, and many other crops to control broad leaf weeds. Unfortunately, it can cause damage to cotton. To ameliorate phytotoxicity of flumioxazin to cotton, this work assessed the protective effects of diazabicyclo derivatives as potential safeners in cotton. A bioactivity assay proved that the phytotoxicity of flumioxazin on cotton was alleviated by some of the compounds. In particular, the activity of glutathione S-transferases (GSTs) was significantly enhanced by Compound 32, which showed good safening activity against flumioxazin injury. The physicochemical properties and absorption, distribution, metabolism, excretion and toxicity (ADMET) predictions proved that the pharmacokinetic properties of Compound 32 are similar to those of the commercial safener BAS 145138. The present work demonstrated that diazabicyclo derivatives are potentially efficacious as herbicide safeners, meriting further investigation.

Heterocyclic Compounds as Dipeptidyl Peptidase-IV Inhibitors with Special Emphasis on Oxadiazoles as Potent Anti-Diabetic Agents

Dipeptidyl peptidase-IV (DPP-IV) inhibitors, often known as gliptins, have been used to treat type 2 diabetes mellitus (T2DM). They may be combined with other medications as an additional treatment or used alone as a monotherapy. In addition to insulin, sulfonylureas, thiazolidinediones, and metformin, these molecules appear as possible therapeutic options. Oxadiazole rings have been employed in numerous different ways during drug development efforts. It has been shown that including them in the pharmacophore increases the amount of ligand that may be bound. The exceptional hydrogen bond acceptor properties of oxadiazoles and the distinct hydrocarbon bonding potential of their regioisomers have been established. Beside their anti-diabetic effects, oxadiazoles display a wide range of pharmacological properties. In this study, we made the assumption that molecules containing oxadiazole rings may afford a different approach to the treatment of diabetes, not only for controlling glycemic levels but also for preventing atherosclerosis progression and other complications associated with diabetes. It was observed that oxadiazole fusion with benzothiazole, 5-(2,5,2-trifluoroethoxy) phenyl, β-homophenylalanine, 2-methyl-2-{5-(4-chlorophenyl), diamine-bridged bis-coumarinyl, 5-aryl-2-(6′-nitrobenzofuran-2′-yl), nitrobenzofuran, and/or oxindole leads to potential anti-diabetic activity.

On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach

A large number of chemical compounds are available in databases such as PubChem and ZINC. However, currently known compounds, though large, represent only a fraction of possible compounds, which is known as chemical space. Many of these compounds in the databases are annotated with properties and assay data that can be used for drug discovery efforts. For this goal, a number of machine learning algorithms have been developed and recent deep learning technologies can be effectively used to navigate chemical space, especially for unknown chemical compounds, in terms of drug-related tasks. In this article, we survey how deep learning technologies can model and utilize chemical compound information in a task-oriented way by exploiting annotated properties and assay data in the chemical compounds databases. We first compile what kind of tasks are trying to be accomplished by machine learning methods. Then, we survey deep learning technologies to show their modeling power and current applications for accomplishing drug related tasks. Next, we survey deep learning techniques to address the insufficiency issue of annotated data for more effective navigation of chemical space. Chemical compound information alone may not be powerful enough for drug related tasks, thus we survey what kind of information, such as assay and gene expression data, can be used to improve the prediction power of deep learning models. Finally, we conclude this survey with four important newly developed technologies that are yet to be fully incorporated into computational analysis of chemical information.

Development of an Automatic Pipeline for Participation in the CELPP Challenge

The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining—whenever possible—empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein–ligand complexes, which will be addressed in future versions of the pipeline.

On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods

Multiparameter optimization, the heart of drug design, is still an open challenge. Thus, improved methods for automated compound design with multiple controlled properties are desired. Here, we present a significant extension to our previously described fragment-based reinforcement learning method (DeepFMPO) for the generation of novel molecules with optimal properties. As before, the generative process outputs optimized molecules similar to the input structures, now with the improved feature of replacing parts of these molecules with fragments of similar three-dimensional (3D) shape and electrostatics. We developed and benchmarked a new python package, ESP-Sim, for the comparison of the electrostatic potential and the molecular shape, allowing the calculation of high-quality partial charges (e.g., RESP with B3LYP/6-31G**) obtained using the quantum chemistry program Psi4. By performing comparisons of 3D fragments, we can simulate 3D properties while overcoming the notoriously difficult step of accurately describing bioactive conformations. The new improved generative (DeepFMPO v3D) method is demonstrated with a scaffold-hopping exercise identifying CDK2 bioisosteres. The code is open-source and freely available.

Bacterial 2'-Deoxyguanosine Riboswitch Classes as Potential Targets for Antibiotics: A Structure and Dynamics Study

The spread of antibiotic-resistant bacteria represents a substantial health threat. Current antibiotics act on a few metabolic pathways, facilitating resistance. Consequently, novel regulatory inhibition mechanisms are necessary. Riboswitches represent promising targets for antibacterial drugs. Purine riboswitches are interesting, since they play essential roles in the genetic regulation of bacterial metabolism. Among these, class I (2′-dG-I) and class II (2′-dG-II) are two different 2′-deoxyguanosine (2′-dG) riboswitches involved in the control of deoxyguanosine metabolism. However, high affinity for nucleosides involves local or distal modifications around the ligand-binding pocket, depending on the class. Therefore, it is crucial to understand these riboswitches’ recognition mechanisms as antibiotic targets. In this work, we used a combination of computational biophysics approaches to investigate the structure, dynamics, and energy landscape of both 2′-dG classes bound to the nucleoside ligands, 2′-deoxyguanosine, and riboguanosine. Our results suggest that the stability and increased interactions in the three-way junction of 2′-dG riboswitches were associated with a higher nucleoside ligand affinity. Also, structural changes in the 2′-dG-II aptamers enable enhanced intramolecular communication. Overall, the 2′-dG-II riboswitch might be a promising drug design target due to its ability to recognize both cognate and noncognate ligands.

Drug-likeness scoring based on unsupervised learning

Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learning model provides drug-likeness scores that well separate distributions with increasing mean values in the order of datasets composed of molecules at a later step in a drug development process, whereas the classification model predicted a polarized distribution with two extreme values for all datasets presumably due to the overconfident prediction for unseen data. Thus, this new concept offers a pragmatic tool for drug-likeness scoring and further can be applied to other biochemical applications.

Dissimilar Ligands Bind in a Similar Fashion: A Guide to Ligand Binding-Mode Prediction with Application to CELPP Studies

The molecular similarity principle has achieved great successes in the field of drug design/discovery. Existing studies have focused on similar ligands, while the behaviors of dissimilar ligands remain unknown. In this study, we developed an intercomparison strategy in order to compare the binding modes of ligands with different molecular structures. A systematic analysis of a newly constructed protein–ligand complex structure dataset showed that ligands with similar structures tended to share a similar binding mode, which is consistent with the Molecular Similarity Principle. More importantly, the results revealed that dissimilar ligands can also bind in a similar fashion. This finding may open another avenue for drug discovery. Furthermore, a template-guiding method was introduced for predicting protein–ligand complex structures. With the use of dissimilar ligands as templates, our method significantly outperformed the traditional molecular docking methods. The newly developed template-guiding method was further applied to recent CELPP studies.

Thermodynamics of semi-specific ligand recognition: the binding of dipeptides to the E.coli dipeptide binding protein DppA

This investigation of the temperature dependence of DppA interactions with a subset of three dipeptides (AA. AF and FA) by isothermal titration calorimetry has revealed the negative heat capacity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta {C}_{p}^{o}$$\end{document}ΔCpo) that is a characteristic of hydrophobic interactions. The observation of enthalpy–entropy compensation is interpreted in terms of the increased structuring of water molecules trapped in a hydrophobic environment, the enthalpic energy gain from which is automatically countered by the entropy decrease associated with consequent loss of water structure flexibility. Specificity for dipeptides stems from appropriate spacing of designated DppA aspartate and arginine residues for electrostatic interaction with the terminal amino and carboxyl groups of a dipeptide, after which the binding pocket closes to become completely isolated from the aqueous environment. Any differences in chemical reactivity of the dipeptide sidechains are thereby modulated by their occurrence in a hydrophobic environment where changes in the structural state of entrapped water molecules give rise to the phenomenon of enthalpy–entropy compensation. The consequent minimization of differences in the value of ΔG⁰ for all DppA–dipeptide interactions thus provides thermodynamic insight into the biological role of DppA as a transporter of all dipeptides across the periplasmic membrane.

Fragmenlt Recombination Design, Synthesis, and Safener Activity of Novel Ester-Substituted Pyrazole Derivatives

Fenoxaprop-p-ethyl (FE), a type of acetyl-CoA carboxylase (ACCase) inhibitor, has been extensively applied to a variety of crop plants. It can cause damage to wheat (Triticum aestivum) even resulting in the death of the crop. On the prerequisite of not reducing herbicidal efficiency on target weed species, herbicide safeners selectively protect crops from herbicide injury. Based on fragment splicing, a series of novel substituted pyrazole derivatives was designed to ultimately address the phytotoxicity to wheat caused by FE. The title compounds were synthesized in a one-pot way and characterized via infrared spectroscopy, ¹H nuclear magnetic resonance, ¹³C nuclear magnetic resonance, and high-resolution mass spectrometry. The bioactivity assay proved that the FE phytotoxicity to wheat could be reduced by most of the title compounds. The molecular docking model indicated that compound IV-21 prevented fenoxaprop acid (FA) from reaching or acting with ACCase. The absorption, distribution, metabolism, excretion, and toxicity predictions demonstrated that compound IV-21 exhibited superior pharmacokinetic properties to the commercialized safener mefenpyr-diethyl. The current work revealed that a series of newly substituted pyrazole derivatives presented strong herbicide safener activity in wheat. This may serve as a potential candidate structure to contribute to the further protection of wheat from herbicide injury.

Fragments recombination, design, synthesis, safener activity and CoMFA model of novel substituted dichloroacetylphenyl sulfonamide derivatives

BACKGROUND

Isoxaflutole (IXF), as a kind of 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitor, has been widely used in many kinds of plants. IXF can cause injury in corn including leaf and stem bleaching, plant height reduction or stunting, and reduced crop stand. Safeners are co-applied with herbicides to protect crops without compromising weed control efficacy. With the ultimate goal of addressing Zea mays injury caused by IXF, a series of novel substituted dichloroacetylphenyl sulfonamide derivatives was designed on the basis of scaffold hopping and active substructure splicing.

RESULTS

A total of 35 compounds were synthesized via acylation reactions. All the compounds were characterized by infrared (IR), proton and carbon-13 nuclear magnetic resonance (¹ H-NMR and ¹³ C-NMR), and high-resolution mass spectrometry (HRMS). The configuration of compound II-1 was confirmed by single crystal X-ray diffraction. The bioassay results showed that all the title compounds displayed remarkable protection against IXF via improved content of carotenoid. Especially compound II-1 which possessed better glutathione transferases (GSTs) activity and carotenoid content than the contrast safener cyprosulfamide (CSA). All the satisfied parameters suggested that the Comparative Molecular Field Analysis (CoMFA) model was reliable and stable [with a cross-validated coefficient (q² ) = 0.527, r² = 0.995, r² _pred = 0.931]. The molecular docking simulation indicated that the compound II-1 and CSA could compete with diketonitrile (DKN) at the active site of HPPD, which is a hydrolyzed product of IXF in plants, causing the herbicide to be ineffective.

CONCLUSIONS

The present work revealed that the compound II-1 deserves further attention as the candidate structure of safeners. © 2020 Society of Chemical Industry.

Tuning Local Hydration Enables a Deeper Understanding of Protein-Ligand Binding: The PP1-Src Kinase Case

Water plays a key role in biomolecular recognition and binding. Despite the development of several computational and experimental approaches, it is still challenging to comprehensively characterize water-mediated effects on the binding process. Here, we investigate how water affects the binding of Src kinase to one of its inhibitors, PP1. Src kinase is a target for treating several diseases, including cancer. We use biased molecular dynamics simulations, where the hydration of predetermined regions is tuned at will. This computational technique efficiently accelerates the SRC-PP1 binding simulation and allows us to identify several key and yet unexplored aspects of the solvent's role. This study provides a further perspective on the binding phenomenon, which may advance the current drug design approaches for the development of new kinase inhibitors.

Significance of 1,3,4-Oxadiazole Containing Compounds in New Drug Development

BACKGROUND

Oxadiazole core displays various pharmacological properties among five membered nitrogen heterocyclic compounds, specially 1,3,4-oxadiazole containing molecules that have occupied a particular place in the field of synthetic medicinal chemistry as surrogates (bioisosteres) of carboxylic acids, carboxamides and esters. Moreover, they are having widespread kind of applications in numerous zones as polymers, as luminescence producing materials and as electron- transporting materials and corrosion inhibitors.

METHODS

This study contains comprehensive and extensive literature survey on chemical reactivity and biological properties associated with 1,3,4-oxadiazole containing compounds.

RESULTS

This review summarises 1,3,4-oxadiazole moiety in numerous compounds with reported pharmacological activity such as antiviral, analgesic and anti-inflammatory, antitumor, antioxidant, insecticidal and anti-parasitic, etc. Nevertheless, ring opening reactions of the 1,3,4-oxadiazole core have also made great attention, as they produce new analogues containing an aliphatic nitrogen atom and to other ring systems.

CONCLUSION

In relation to the occurrence of oxadiazoles in biologically active molecules, 1,3,4- oxadiazole core emerges as a structural subunit of countless significance and usefulness for the development of new drug aspirants applicable to the treatment of many diseases. It concludes that 1,3,4-oxadiazole core compounds are more efficacious and less toxic medicinal agents with respect to new opinions in the search for rational strategies.

Shared Binding Mode of Perrottetinene and Tetrahydrocannabinol Diastereomers inside the CB1 Receptor May Incentivize Novel Medicinal Drug Design: Findings from an in Silico Assay

In recent years, therapeutic compounds derived from phytocannabinoids have brought renewed attention to the benefits they offer to ameliorate chronic disease symptoms. Among cannabinoids, tetrahydrocannabinol (THC) is a well-known component of the Cannabis plant, whose active principles have been studied through the years. Another psychoactive phytocannabinoid, derived from liverworts Radula, perrottetinene (PET), has created interest, especially as a pharmaceutical product and for its legal recreational use. Unfortunately, so far, the interaction mode of these compounds at the type 1 cannabinoid receptors (CB1R) binding site remains unknown, and no experimental three-dimensional structure in complex with THC or PET is available in the Protein Data Bank. Today, many computational methodologies can assist in this crusade and help unveil how these molecules bind, based on the already known pose of a structurally similar compound. In this work, we aim to elucidate the binding mode of THC and PET molecules in both cis and trans conformers, using a combination of several computational methodologies, including molecular docking, molecular dynamics, free energy calculations, and protein-energy network studies. We found that THC and PET interact similarly with the CB1R, in a different conformation depending on the considered diastereomer. We have observed that cis ligands adopted a half-chair conformation of the cycle ring containing the dimethyl group, assuming an axial or equatorial conformation producing a different induced fitting of the surrounding residues compared with trans ligands, with higher interaction energy than the trans conformer. For PET, we have seen that Trp-279 and Trp-356 have a marked influence on the binding. After binding, Trp-279 accommodates its side chain to better interact with the PET's terminal phenyl group, disturbing CB1R residues communication. The interaction with Trp-356 might impair the activation of CB1R and can influence the binding of PET as a partial agonist. Understanding the PET association with CB1R from a molecular perspective can offer a glimpse of preventing potential toxicological or recreational effects since it is an attractive lead for drug development with fewer side effects than trans-THC.

Groundbreaking Anticancer Activity of Highly Diversified Oxadiazole Scaffolds

Nowadays, an increasing number of heterocyclic-based drugs found application in medicinal chemistry and, in particular, as anticancer agents. In this context, oxadiazoles—five-membered aromatic rings—emerged for their interesting biological properties. Modification of oxadiazole scaffolds represents a valid strategy to increase their anticancer activity, especially on 1,2,4 and 1,3,4 regioisomers. In the last years, an increasing number of oxadiazole derivatives, with remarkable cytotoxicity for several tumor lines, were identified. Structural modifications, that ensure higher cytotoxicity towards malignant cells, represent a solid starting point in the development of novel oxadiazole-based drugs. To increase the specificity of this strategy, outstanding oxadiazole scaffolds have been designed to selectively interact with biological targets, including enzymes, globular proteins, and nucleic acids, showing more promising antitumor effects. In the present work, we aim to provide a comprehensive overview of the anticancer activity of these heterocycles, describing their effect on different targets and highlighting how their structural versatility has been exploited to modulate their biological properties.

The putative binding site and SAR rationalization of small molecules against glucagon-like peptide-1 receptor using homology model and crystal structures: a comparative study

Glucagon-like peptide-1 (GLP-1) is involved in glucose-stimulated insulin secretion and weight regulating actions through the activation of the GLP-1 receptor (GLP-1R). Clinical effectiveness of GLP-1 mimetics is effective in improving glucose control in patients. Thus, identifying and developing orally active small-molecule agonists are highly desirable. This study summarizes the structure-function relationship of hGLP-1R through computational approaches and search of small molecule GLP-1R agonists. We carried out mutation guided data-driven study, for developing the GLP-1R model to explore and validate the putative site for quinoxaline analogues. The developed GLP-1R homology model was subjected to 500 ns MD simulation for validation. Various snapshots were considered to identify the best structure of GLP-1R based on correlation between experimental pEC₅₀ and various theoretical parameters (docking score, MM-GBSA ΔG bind, WM/MM ΔG bind). The putative binding site (Sitemap and WaterMap has been predicted and it matched well with the available data. Excellent correlation (R² =0.94), between pEC50 and WM/MM ΔG bind for the snapshot at 350 ns was observed after including induced-fit docking results of the most potent molecule. Enrichment calculation indicates better AUC (=0.75) for predicted complex structure. A comparison of the developed GLP-1R model with the available crystal structure shows excellent similarities and it was used for virtual screening to find small molecule agonists. The good correlation of our model with crystal structures of GLP-1R may help to understand the structure-function relationship of other secretin families.

TF3P: Three-Dimensional Force Fields Fingerprint Learned by Deep Capsular Network

Molecular fingerprints are the workhorse in ligand-based drug discovery. In recent years, an increasing number of research papers reported fascinating results on using deep neural networks to learn 2D molecular representations as fingerprints. It is anticipated that the integration of deep learning would also contribute to the prosperity of 3D fingerprints. Here, we unprecedentedly introduce deep learning into 3D small molecule fingerprints, presenting a new one we termed as the three-dimensional force fields fingerprint (TF3P). TF3P is learned by a deep capsular network whose training is in no need of labeled data sets for specific predictive tasks. TF3P can encode the 3D force fields information of molecules and demonstrates the stronger ability to capture 3D structural changes, to recognize molecules alike in 3D but not in 2D, and to identify similar targets inaccessible by other 2D or 3D fingerprints based on only ligands similarity. Furthermore, TF3P is compatible with both statistical models (e.g., similarity ensemble approach) and machine learning models. Altogether, we report TF3P as a new 3D small molecule fingerprint with a promising future in ligand-based drug discovery. All codes are written in Python and available at https://github.com/canisw/tf3p.

Exploration of carbamide derived pyrimidine-thioindole conjugates as potential VEGFR-2 inhibitors with anti-angiogenesis effect

The development of new small molecules from known structural motifs through molecular hybridization is one of the trends in drug discovery. In this connection, we have combined the two pharmacophoric units (pyrimidine and thioindole) in a single entity via molecular hybridization strategy along with introduction of urea functionality at C2 position of pyrimidine to increase the efficiency of H-bonding interactions. Among the synthesized conjugates 12a-aa, compound 12k was found to exhibit significant IC₅₀ values 5.85, 7.87, 6.41 and 10.43 μM against MDA-MB-231 (breast), HepG2 (liver), A549 (lung) and PC-3 (prostate) cancer cell lines, respectively. All these compounds were further evaluated for their inhibitory activities against VEGFR-2 protein. The results specified that among the tested compounds, 12d, 12e, 12k, 12l, 12p, 12q, 12t and 12u prominently suppressed VEGFR-2, with IC₅₀ values of 310-920 nM in association to the positive control (210 nM). Angiogenesis inhibition was evident by tube formation assay in HUVECs and cell-invasion by transwell assay. The mechanism of cellular toxicity on MDA-MB-231 was found through depolarisation of mitochondrial membrane potential, increased ROS production and subsequent DNA damage resulting in apoptosis induction. Moreover, clonogenic and wound healing assays designated the inhibition of colony formation and cell migration by 12k in a dose-dependent manner. Molecular docking studies also shown that compound 12k capably intermingled with catalytically active residues GLU-885, ASP-1046 of the VEGFR-2 through hydrogen-bonding interactions.

Identification of Selective Inhibitors of LdDHFR Enzyme Using Pharmacoinformatic Methods

Dihydrofolate reductase (DHFR) is a well-known enzyme of the folate metabolic pathway and it is a validated drug target for leishmaniasis. However, only a few leads are reported against Leishmania donovani DHFR (LdDHFR), and thus, there is a need to identify new inhibitors. In this article, pharmacoinformatic tools such as molecular docking, virtual screening, absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling, and molecular dynamics (MD) simulations were utilized to identify potential LdDHFR inhibitors. Initially, a natural DHFR substrate (dihydrofolate), a classical DHFR inhibitor (methotrexate), and a potent LdDHFR inhibitor, that is, "5-(3-(octyloxy)benzyl)pyrimidine-2,4-diamine" (LEAD) were docked in the active site of the LdDHFR and MD simulated to understand the binding mode characteristics of the substrates/inhibitors in the LdDHFR. The shape of the LEAD molecule was used as a query for shape-based virtual screening, while the three-dimensional structure of LdDHFR was utilized for docking-based virtual screening. In silico ADMET factors were also considered during virtual screening. These two screening processes yielded 25 suitable hits, which were further validated for their selectivity toward LdDHFR using molecular docking and prime molecular mechanics/generalized born surface area analysis in the human DHFR (HsDHFR). Best six hits, which were selective and energetically favorable for the LdDHFR, were chosen for MD simulations. The MD analysis showed that four of the hits exhibited very good binding affinity for LdDHFR with respect to HsDHFR, and two hits were found to be more selective than the reported potent LdDHFR inhibitor. The present study thus identifies hits that can be further designed and modified as potent LdDHFR inhibitors.

Identification of Target Associations for Polypharmacology from Analysis of Crystallographic Ligands of the Protein Data Bank

The design of a chemical entity that potently and selectively binds to a biological target of therapeutic relevance has dominated the scene of drug discovery so far. However, recent findings suggest that multitarget ligands may be endowed with superior efficacy and be less prone to drug resistance. The Protein Data Bank (PDB) provides experimentally validated structural information about targets and bound ligands. Therefore, it represents a valuable source of information to help identifying active sites, understanding pharmacophore requirements, designing novel ligands, and inferring structure-activity relationships. In this study, we performed a large-scale analysis of the PDB by integrating different ligand-based and structure-based approaches, with the aim of identifying promising target associations for polypharmacology based on reported crystal structure information. First, the 2D and 3D similarity profiles of the crystallographic ligands were evaluated using different ligand-based methods. Then, activity data of pairs of similar ligands binding to different targets were inspected by comparing structural information with bioactivity annotations reported in the ChEMBL, BindingDB, BindingMOAD, and PDBbind databases. Afterward, extensive docking screenings of ligands in the identified cross-targets were made in order to validate and refine the ligand-based results. Finally, the therapeutic relevance of the identified target combinations for polypharmacology was evaluated from comparison with information on therapeutic targets reported in the Therapeutic Target Database (TTD). The results led to the identification of several target associations with high therapeutic potential for polypharmacology.

MetaMed: Linking Microbiota Functions with Medicine Therapeutics

Understanding how the human microbiome affects human health has consequences for treating disease and minimizing unwanted side effects in clinical research.

Understanding how the human microbiome affects human health has consequences for treating disease and minimizing unwanted side effects in clinical research. Here, we present MetaMed (http://metamed.rwebox.com/index), a novel and integrative system-wide correlation mapping system to link bacterial functions and medicine therapeutics, providing novel hypotheses for deep investigation of microbe therapeutic effects on human health. Furthermore, comprehensive relationships between microbes living in the environment and drugs were discovered, providing a rich source for discovering microbiota metabolites with great potential for pharmaceutical applications.

Prediction of water positions in the binding sites of proteins based on collections of multi-source heterogeneous atoms

Water molecules play an important role in mediating the interactions between proteins and ligands. However, it is difficult to distinguish the key water molecules directly because they are widely and irregularly distributed. Based on the results of statistical analysis, a composite tetrahedral model is proposed to predict the potential hydration sites in the binding sites of crystal structures. By analyzing the different protein atoms and ligand atoms that interact with water molecules, the unified representation and measurement of these multi-source heterogeneous atoms in the multi-dimensional feature space were adopted. The potential hydration sites could be predicted based on the results of the preference analysis and the shape-matching method. A test set was used to evaluate the model performance and extensive comparison with the tetrahedral-water-cluster model and Dowser++ revealed that the composite tetrahedral model can not only predict the potential sites of multiple key water molecules in the binding sites but also has a better prediction accuracy.

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design

In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modeled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improving these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output toward structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid and more than a third satisfy the targeted objectives, while there were none in the initial set.

TCM-ADMEpred: A novel strategy for poly-pharmacokinetics prediction of traditional Chinese medicine based on single constituent pharmacokinetics, structural similarity, and mathematical modeling

ETHNOPHARMACOLOGICAL RELEVANCE

Yuanhu Zhitong prescription (YZP) is a commonly used and relatively simple clinical herb preparation recorded in the China Pharmacopoeia. It contains Corydalis yanhusuo (Chinese name, Yanhusuo [YH]) and Angelica dahurica (Hoffm.) (Chinese name, Baizhi [BZ]), and has a long history of use in traditional Chinese medicine (TCM) for the treatment of stomach pain, hypochondriac pain, headache, and dysmenorrhea.

AIM OF THE STUDY

A TCM-ADMEpred method is developed for novel strategy for poly-pharmacokinetics prediction of TCM. To predict the pharmacokinetic characteristics of the main YZP constituents in rat plasma using in silico models, based on the theory that structurally similar constituents show similar pharmacokinetic properties. This approach may facilitate in silico prediction of the pharmacokinetics of TCM.

MATERIALS AND METHODS

A robust platform using ultra-performance liquid chromatography coupled with triple quadrupole electrospray tandem mass spectrometry (UPLC-ESI-MS/MS) was developed and validated for simultaneous determination of seven active YZP constituents in rat plasma. These seven compounds were divided into two structural classes, alkaloids and coumarins. The correlation between AUC profiles within a structural class was expressed as Γ⁺, and this variable was used to develop two novel in silico models to predict constituent AUC values. The pharmacokinetics of tetrahydropalmatine, tetrahydroberberine, and corydaline following YZP administration were predicted using the Γ⁺-values of α-allocryptopine observed following YH administration, while those of imperatorin and isoimperatorin following BZ administration were predicted using the Γ⁺-values of byakangelicin observed following YZP administration.

RESULTS

The UPLC-ESI-MS/MS method was successfully used to evaluate pharmacokinetic parameters after oral YZP, YH, or BZ administration. Our findings showed that co-administration of YH and BZ increased the AUC of four alkaloid constituents and reduced the AUC of three coumarin constituents, which might provide a scientific rationale for co-administering these herbs clinically as a YZP preparation, thus increasing their efficacy and reducing toxicity. The AUC values of imperatorin and isoimperatorin were predicted 3 h after oral BZ administration, with the bias ratios between the theoretical values and the observed experimental values ranging from 0.61% to 11.4%, and average bias ratios of 5.8% and 8.0%, respectively. The AUC values of tetrahydropalmatine, tetrahydroberberine, and corydaline were predicted 3 h after oral YZP administration, with bias ratios ranging from 3.7% to 46.4%, and average bias ratios of 23.8%, 15.4%, and 25.8%, respectively.

CONCLUSION

The UPLC-ESI-MS/MS method was successfully applied to pharmacokinetic evaluations after oral administration of YZP, YH, and BZ to rats. The Γ⁺ variable was used to express the correlation between the AUC profiles of structurally similar compounds. This facilitated the development of an in silico model that was used to predict the AUC of three alkaloids in YZP and of two coumarins in BZ. Calculation of the bias ratios between the predicted and experimental values suggested that this in silico model provided a viable approach for the prediction of TCM pharmacokinetics.

The Varieties of the Psychedelic Experience: A Preliminary Study of the Association Between the Reported Subjective Effects and the Binding Affinity Profiles of Substituted Phenethylamines and Tryptamines

Classic psychedelics are substances of paramount cultural and neuroscientific importance. A distinctive feature of psychedelic drugs is the wide range of potential subjective effects they can elicit, known to be deeply influenced by the internal state of the user ("set") and the surroundings ("setting"). The observation of cross-tolerance and a series of empirical studies in humans and animal models support agonism at the serotonin (5-HT)2A receptor as a common mechanism for the action of psychedelics. The diversity of subjective effects elicited by different compounds has been attributed to the variables of "set" and "setting," to the binding affinities for other 5-HT receptor subtypes, and to the heterogeneity of transduction pathways initiated by conformational receptor states as they interact with different ligands ("functional selectivity"). Here we investigate the complementary (i.e., not mutually exclusive) possibility that such variety is also related to the binding affinity for a range of neurotransmitters and monoamine transporters including (but not limited to) 5-HT receptors. Building on two independent binding affinity datasets (compared to "in silico" estimates) in combination with natural language processing tools applied to a large repository of reports of psychedelic experiences (Erowid's Experience Vaults), we obtained preliminary evidence supporting that the similarity between the binding affinity profiles of psychoactive substituted phenethylamines and tryptamines is correlated with the semantic similarity of the associated reports. We also showed that the highest correlation was achieved by considering the combined binding affinity for the 5-HT, dopamine (DA), glutamate, muscarinic and opioid receptors and for the Ca+ channel. Applying dimensionality reduction techniques to the reports, we linked the compounds, receptors, transporters and the Ca+ channel to distinct fingerprints of the reported subjective effects. To the extent that the existing binding affinity data is based on a low number of displacement curves that requires further replication, our analysis produced preliminary evidence consistent with the involvement of different binding sites in the reported subjective effects elicited by psychedelics. Beyond the study of this particular class of drugs, we provide a methodological framework to explore the relationship between the binding affinity profiles and the reported subjective effects of other psychoactive compounds.

Predictive classification models and targets identification for betulin derivatives as Leishmania donovani inhibitors

Betulin derivatives have been proven effective in vitro against Leishmania donovani amastigotes, which cause visceral leishmaniasis. Identifying the molecular targets and molecular mechanisms underlying their action is a currently an unmet challenge. In the present study, we tackle this problem using computational methods to establish properties essential for activity as well as to screen betulin derivatives against potential targets. Recursive partitioning classification methods were explored to develop predictive models for 58 diverse betulin derivatives inhibitors of L. donovani amastigotes. The established models were validated on a testing set, showing excellent performance. Molecular fingerprints FCFP_6 and ALogP were extracted as the physicochemical properties most extensively involved in separating inhibitors from non-inhibitors. The potential targets of betulin derivatives inhibitors were predicted by in silico target fishing using structure-based pharmacophore searching and compound-pharmacophore-target-pathway network analysis, first on PDB and then among L. donovani homologs using a PSI-BLAST search. The essential identified proteins are all related to protein kinase family. Previous research already suggested members of the cyclin-dependent kinase family and MAP kinases as Leishmania potential drug targets. The PSI-BLAST search suggests two L. donovani proteins to be especially attractive as putative betulin target, heat shock protein 83 and membrane transporter D1.

Orthosteric and benzodiazepine cavities of the α1β2γ2 GABAA receptor: insights from experimentally validated in silico methods

γ-aminobutyric acid-type A (GABA_A) receptors mediate fast synaptic inhibition in the central nervous system of mammals. They are modulated via several sites by numerous compounds, which include GABA, benzodiazepines, ethanol, neurosteroids and anaesthetics among others. Due to their potential as targets of novel drugs, a detailed knowledge of their structure-function relationships is needed. Here, we present the model of the α₁β₂γ₂ subtype GABA_A receptor in the APO state and in complex with selected ligands, including agonists, antagonists and allosteric modulators. The model is based on the crystallographic structure of the human β₃ homopentamer GABA_A receptor. The complexes were refined using atomistic molecular dynamics simulations. This allowed a broad description of the binding modes and the detection of important interactions in agreement with experimental information. From the best of our knowledge, this is the only model of the α₁β₂γ₂ GABA_A receptor that represents altogether the desensitized state of the channel and comprehensively describes the interactions of ligands of the orthosteric and benzodiazepines binding sites in agreement with the available experimental data. Furthermore, it is able to explain small differences regarding the binding of a variety of chemically divergent ligands. Finally, this new model may pave the way for the design of focused experimental studies that will allow a deeper description of the receptor.

RosettaLigandEnsemble: A Small-Molecule Ensemble-Driven Docking Approach

RosettaLigand is a protein-small-molecule (ligand) docking software capable of predicting binding poses and is used for virtual screening of medium-sized ligand libraries. Structurally similar small molecules are generally found to bind in the same pose to one binding pocket, despite some prominent exceptions. To make use of this information, we have developed RosettaLigandEnsemble (RLE). RLE docks a superimposed ensemble of congeneric ligands simultaneously. The program determines a well-scoring overall pose for this superimposed ensemble before independently optimizing individual protein-small-molecule interfaces. In a cross-docking benchmark of 89 protein-small-molecule co-crystal structures across 20 biological systems, we found that RLE improved sampling efficiency in 62 cases, with an average change of 18%. In addition, RLE generated more consistent docking results within a congeneric series and was capable of rescuing the unsuccessful docking of individual ligands, identifying a nativelike top-scoring model in 10 additional cases. The improvement in RLE is driven by a balance between having a sizable common chemical scaffold and meaningful modifications to distal groups. The new ensemble docking algorithm will work well in conjunction with medicinal chemistry structure-activity relationship (SAR) studies to more accurately recapitulate protein-ligand interfaces. We also tested whether optimizing the rank correlation of RLE-binding scores to SAR data in the refinement step helps the high-resolution positioning of the ligand. However, no significant improvement was observed.

Predictive Power of Different Types of Experimental Restraints in Small Molecule Docking: A Review

Incorporating experimental restraints is a powerful method of increasing accuracy in computational protein small molecule docking simulations. Different algorithms integrate distinct forms of biochemical data during the docking and/or scoring stages. These so-called hybrid methods make use of receptor-based information such as nuclear magnetic resonance (NMR) restraints or small molecule-based information such as structure-activity relationships (SARs). A third class of methods directly interrogates contacts between the protein receptor and the small molecule. This work reviews the current state of using such restraints in docking simulations, evaluates their feasibility across broad systems, and identifies potential areas of algorithm development.

Studies of 4-arylthiazolylhydrazones derived from 1-indanones as Trypanosoma cruzi squalene epoxidase inhibitors by molecular simulations

We present for the first time a study at the molecular level of the T. cruzi SE and the structural requirements for its inhibition.

Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2

Two of the major ongoing challenges in computational drug discovery are predicting the binding pose and affinity of a compound to a protein. The Drug Design Data Resource Grand Challenge 2 was developed to address these problems and to drive development of new methods. The challenge provided the 2D structures of compounds for which the organizers help blinded data in the form of 35 X-ray crystal structures and 102 binding affinity measurements and challenged participants to predict the binding pose and affinity of the compounds. We tested a number of pose prediction methods as part of the challenge; we found that docking methods that incorporate protein flexibility (Induced Fit Docking) outperformed methods that treated the protein as rigid. We also found that using binding pose metadynamics, a molecular dynamics based method, to score docked poses provided the best predictions of our methods with an average RMSD of 2.01 Å. We tested both structure-based (e.g. docking) and ligand-based methods (e.g. QSAR) in the affinity prediction portion of the competition. We found that our structure-based methods based on docking with Smina (Spearman ρ = 0.614), performed slightly better than our ligand-based methods (ρ = 0.543), and had equivalent performance with the other top methods in the competition. Despite the overall good performance of our methods in comparison to other participants in the challenge, there exists significant room for improvement especially in cases such as these where protein flexibility plays such a large role.

Performance of machine-learning scoring functions in structure-based virtual screening

Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets. We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment. Our results show RF-Score-VS can substantially improve virtual screening performance: RF-Score-VS top 1% provides 55.6% hit rate, whereas that of Vina only 16.2% (for smaller percent the difference is even more encouraging: RF-Score-VS top 0.1% achieves 88.6% hit rate for 27.5% using Vina). In addition, RF-Score-VS provides much better prediction of measured binding affinity than Vina (Pearson correlation of 0.56 and −0.18, respectively). Lastly, we test RF-Score-VS on an independent test set from the DEKOIS benchmark and observed comparable results. We provide full data sets to facilitate further research in this area (http://github.com/oddt/rfscorevs) as well as ready-to-use RF-Score-VS (http://github.com/oddt/rfscorevs_binary).

Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification-Salvinorin A as a case study

Drug abuse is a serious problem worldwide. Recently, hallucinogens have been reported as a potential preventative and auxiliary therapy for substance abuse. However, the use of hallucinogens as a drug abuse treatment has potential risks, as the fundamental mechanisms of hallucinogens are not clear. So far, no scientific database is available for the mechanism research of hallucinogens. We constructed a hallucinogen-specific chemogenomics database by collecting chemicals, protein targets and pathways closely related to hallucinogens. This information, together with our established computational chemogenomics tools, such as TargetHunter and HTDocking, provided a one-step solution for the mechanism study of hallucinogens. We chose salvinorin A, a potent hallucinogen extracted from the plant Salvia divinorum, as an example to demonstrate the usability of our platform. With the help of HTDocking program, we predicted four novel targets for salvinorin A, including muscarinic acetylcholine receptor 2, cannabinoid receptor 1, cannabinoid receptor 2 and dopamine receptor 2. We looked into the interactions between salvinorin A and the predicted targets. The binding modes, pose and docking scores indicate that salvinorin A may interact with some of these predicted targets. Overall, our database enriched the information of systems pharmacological analysis, target identification and drug discovery for hallucinogens.

Two Binding Geometries for Risperidone in Dopamine D3 Receptors: Insights on the Fast-Off Mechanism through Docking, Quantum Biochemistry, and Molecular Dynamics Simulations

Risperidone is an atypical antipsychotic used in the treatment of schizophrenia and of symptoms of irritability associated with autism spectrum disorder (ASD). Its main action mechanism is the blockade of D2-like receptors acting over positive and negative symptoms of schizophrenia with small risk of extrapyramidal symptoms (EPS) at doses corresponding to low/moderate D2 occupancy. Such a decrease in the side effect incidence can be associated with its fast unbinding from D2 receptors in the nigrostriatal region allowing the recovery of dopamine signaling pathways. We performed docking essays using risperidone and the D3 receptor crystallographic data and results suggested two possible distinct orientations for risperidone at the binding pocket. Orientation 1 is more close to the opening of the binding site and has the 6-fluoro-1,2 benzoxazole fragment toward the bottom of the D3 receptor cleft, while orientation 2 is deeper inside the binding pocket with the same fragment toward to the receptor surface. In order to unveil the implications of these two binding orientations, classical molecular dynamics and quantum biochemistry computations within the density functional theory formalism and the molecular fractionation with conjugate caps framework were performed. Quantum mechanics/molecular mechanics suggests that orientation 2 (considering the contribution of Glu90) is slightly more energetically stable than orientation 1 with the main contribution coming from residue Asp110. The residue Glu90, positioned at the opening of the binding site, is closer to orientation 1 than 2, suggesting that it may have a key role in stability through attractive interaction with risperidone. Therefore, although orientations 1 and 2 are both likely to occur, we suggest that the occurrence of the first may contribute to the reduction of side effects in patients taking risperidone due to the reduction of dopamine receptor occupancy in the nigrostriatal region through a mechanism of fast dissociation. The atypical effect may be obtained simply by either delaying D3R full blockage by spatial hindrance of orientation 1 at the binding site or through an effective blockade followed by orientation 1 fast dissociation. While the molecular interpretation suggested in this work shed some light on the potential molecular mechanisms accounting for the reduced extrapyramidal symptoms observed during risperidone treatment, further studies are necessary in order to evaluate the implications of both orientations during the receptor activation/inhibition. Altogether these data highlight important hot spots in the dopamine receptor binding site bringing relevant information for the development of novel/derivative agents with atypical profile.