Conformational sampling of druglike molecules with MOE and catalyst: implications for pharmacophore modeling and virtual screening

ConfRank: Improving GFN-FF Conformer Ranking with Pairwise Training

Conformer ranking is a crucial task for drug discovery, with methods for generating conformers often based on molecular (meta)dynamics or sophisticated sampling techniques. These methods are constrained by the underlying force computation regarding runtime and energy ranking accuracy, limiting their effectiveness for large-scale screening applications. To address these ranking limitations, we introduce ConfRank, a machine learning-based approach that enhances conformer ranking using pairwise training. We demonstrate its performance using GFN-FF-generated conformer ensembles, leveraging the DimeNet++ architecture trained on pairs of 159 760 uncharged organic compounds from the GEOM data set with r2SCAN-3c reference level. Instead of predicting only on single molecules, this approach captures relative energy differences between conformers, leading to a significant improvement of the overall conformational ranking, outperforming GFN-FF and GFN2-xTB. Thereby, the pairwise RMSD of the relative energy difference of two conformers can be reduced from 5.65 to 0.71 kcal mol-1 on the test data set, allowing to correctly identify up to 81% of all lowest lying conformers correctly (GFN-FF: 10%, GFN2-xTB: 47%). The ConfRank approach is cost-effective, allowing for scalable deployment on both CPU and GPU, achieving runtime accelerations by up to 2 orders of magnitude compared to GFN2-xTB. Out-of-sample investigations on CREST-generated conformer ensembles from the QM9 data set and conformers taken from an extended GMTKN55 data set show promising results for the robustness of this approach. Thereby, ranking correlation coefficient such as Spearman can be improved to 0.90 (GFN-FF: 0.39, GFN2-xTB: 0.84) reducing the probability of an incorrect sign flip in pairwise energy comparison from 32 to 7%. On the extended GMTKN55 subsets the pairwise MAD (RMSD) could be reduced on almost all subsets by up to 62% (58%) with an average improvement of 30% (29%). Moreover, an exemplary case study on vancomycin shows similar performance, indicating applicability to larger (bio)molecular structures. Furthermore, we motivate the usage of the pairwise training approach from a theoretical perspective, highlighting that while pairwise training can lead to a decline in single sample prediction of absolute energies for ML models, it significantly enhances conformer ranking performance. The data and models used in this study are available at https://github.com/grimme-lab/confrank.

Conformational energies of reference organic molecules: benchmarking of common efficient computational methods against coupled cluster theory

We selected 145 reference organic molecules that include model fragments used in computer-aided drug design. We calculated 158 conformational energies and barriers using force fields, with wide applicability in commercial and free softwares and extensive application on the calculation of conformational energies of organic molecules, e.g. the UFF and DREIDING force fields, the Allinger's force fields MM3-96, MM3-00, MM4-8, the MM2-91 clones MMX and MM+, the MMFF94 force field, MM4, ab initio Hartree-Fock (HF) theory with different basis sets, the standard density functional theory B3LYP, the second-order post-HF MP2 theory and the Domain-based Local Pair Natural Orbital Coupled Cluster DLPNO-CCSD(T) theory, with the latter used for accurate reference values. The data set of the organic molecules includes hydrocarbons, haloalkanes, conjugated compounds, and oxygen-, nitrogen-, phosphorus- and sulphur-containing compounds. We reviewed in detail the conformational aspects of these model organic molecules providing the current understanding of the steric and electronic factors that determine the stability of low energy conformers and the literature including previous experimental observations and calculated findings. While progress on the computer hardware allows the calculations of thousands of conformations for later use in drug design projects, this study is an update from previous classical studies that used, as reference values, experimental ones using a variety of methods and different environments. The lowest mean error against the DLPNO-CCSD(T) reference was calculated for MP2 (0.35 kcal mol-1), followed by B3LYP (0.69 kcal mol-1) and the HF theories (0.81-1.0 kcal mol-1). As regards the force fields, the lowest errors were observed for the Allinger's force fields MM3-00 (1.28 kcal mol-1), ΜΜ3-96 (1.40 kcal mol-1) and the Halgren's MMFF94 force field (1.30 kcal mol-1) and then for the MM2-91 clones MMX (1.77 kcal mol-1) and MM+ (2.01 kcal mol-1) and MM4 (2.05 kcal mol-1). The DREIDING (3.63 kcal mol-1) and UFF (3.77 kcal mol-1) force fields have the lowest performance. These model organic molecules we used are often present as fragments in drug-like molecules. The values calculated using DLPNO-CCSD(T) make up a valuable data set for further comparisons and for improved force field parameterization.

Recent Advances in Toxicity Prediction: Applications of Deep Graph Learning

The development of new drugs is time-consuming and expensive, and as such, accurately predicting the potential toxicity of a drug candidate is crucial in ensuring its safety and efficacy. Recently, deep graph learning has become prevalent in this field due to its computational power and cost efficiency. Many novel deep graph learning methods aid toxicity prediction and further prompt drug development. This review aims to connect fundamental knowledge with burgeoning deep graph learning methods. We first summarize the essential components of deep graph learning models for toxicity prediction, including molecular descriptors, molecular representations, evaluation metrics, validation methods, and data sets. Furthermore, based on various graph-related representations of molecules, we introduce several representative studies and methods for toxicity prediction from the perspective of GNN architectures and graph pretrained models. Compared to other types of models, deep graph models not only advance in higher accuracy and efficiency but also provide more intuitive insights, which is significant in the development of model interpretation and generalization ability. The graph pretrained models are emerging as they can extract prominent features from large-scale unlabeled molecular graph data and improve the performance of downstream toxicity prediction tasks. We hope this survey can serve as a handbook for individuals interested in exploring deep graph learning for toxicity prediction.

Virtual screening and structure-activity relationship study of novel BTK inhibitors in Traditional Chinese Medicine for the treatment of rheumatoid arthritis

Bruton tyrosine kinase (BTK) is a known drug target for the treatment of autoimmune diseases, including rheumatoid arthritis (RA). In this study, a series of 1-amino-1H-imidazole-5-carboxamide derivatives with good inhibitory activity against BTK were selected to explore the structure-activity relationships of these BTK inhibitors (BTKIs). Furthermore, we concentrated on 182 prescriptions of Traditional Chinese Medicine with therapeutic effects on RA. 54 herbs with a frequency of ≥10 were counted to establish a database containing 4027 ingredients for virtual screening. Five compounds with relatively higher docking scores and better absorption, distribution, metabolism, elimination and toxicity (ADMET) parameters were then selected for higher precision docking. The results demonstrated that the potentially active molecules form hydrogen bond interactions with the hinge region residues Met477, Glu475, glycine-rich P-loop residue Val416, Lys430 and DFG motif Asp539. In particular, they also interact with the key residues Thr474 and Cys481 of BTK. The molecular dynamics (MD) results demonstrated that all five compounds above could bind with BTK stably as its cognate ligand in dynamic conditions. This work identified several potential BTKIs using a computer-aided drug design approach and may provide crucial information for developing novel BTKIs.Communicated by Ramaswamy H. Sarma.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

Structure-based pharmacophore modeling 1. Automated random pharmacophore model generation

Pharmacophores are three-dimensional arrangements of molecular features required for biological activity that are often used in virtual screening efforts to prioritize ligands for experimental testing. G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for ligand discovery and drug development. Ligand-based pharmacophore models can be constructed to identify structural commonalities between known bioactive ligands for targets including GPCR. However, structure-based pharmacophores (which only require an experimentally determined or modeled structure for a protein target) have gained more attention to aid in virtual screening efforts as the number of publicly available experimentally determined GPCR structures have increased (140 unique GPCR represented as of October 24, 2022). Thus, the goal of this study was to develop a method of structure-based pharmacophore model generation applicable to ligand discovery for GPCR that have few known ligands. Pharmacophore models were generated within the active sites of 8 class A GPCR crystal structures via automated annotation of 5 randomly selected functional group fragments to sample diverse combinations of pharmacophore features. Each of the 5000 generated pharmacophores was then used to search a database containing active and decoy/inactive compounds for 30 class A GPCR and scored using enrichment factor and goodness-of-hit metrics to assess performance. Application of this method to the set of 8 class A GPCR produced pharmacophore models possessing the theoretical maximum enrichment factor value in both resolved structures (8 of 8 cases) and homology models (7 of 8 cases), indicating that generated pharmacophore models can prove useful in the context of virtual screening.

Chemical composition/pharmacophore modelling- based, virtual screening, molecular docking and dynamic simulation studies for the discovery of novel superoxide dismutase (SODs) of bioactive molecules from aerial parts of Inula Montana as antioxydant's agents

The accumulation of free radicals in the body develops chronic and degenerative diseases such as cancer, autoimmune diseases, rheumatoid arthritis, cardiovascular and neurodegenerative diseases. The first aim of this work was to study the chemical composition of Inula Montana essential oil using GC-FID and GC/MS analysis and the antioxidant activities using radical scavenging (DPPH) and the Ferric -Reducing Antioxidant Power (FRAP) tests. The second aim was to describe the assess the antioxidant activity and computational study of Superoxide Dismutase (SODs) and ctDNA inhibition. Sixty-nine compounds were identified in the essential oil of the aerial part of Inula montana. Shyobunol and α-Cadinol were the major compounds in the essential oil. The antioxidant power of the essential oil showed an important antioxidant effect compared to ascorbic acid and the methionine co-crystallized inhibitor. The results of the docking simulation revealed that E, E-Farnesyl acetate has an affinity to interact with binding models and the antioxidant activities of the ctDNA sequence and Superoxide Dismutase target. The penetration through the Blood-Brain Barrier came out to be best for E, E-Farnesyl acetate and E-Nerolidolacetate and was significantly higher than the control molecule and Lref. Finally, the application of ADMET filters gives us positive information on the compound E, E-Farnesyl acetate, which appears as a new inhibitor potentially more active towards ctDNA and SODs target. The active compounds, E,E-Farnesyl acetate can be used as templates for further development of more potent antioxidative agents.Communicated by Ramaswamy H. Sarma.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Application of Computational Biology and Artificial Intelligence in Drug Design

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

Identification of potential interleukin-8 inhibitors acting on the interactive site between chemokine and CXCR2 receptor: A computational approach

Interactions between interleukin (IL)-8 and its receptors, CXCR1, and CXCR2, serve crucial roles in inflammatory conditions and various types of cancers. Inhibition of this signaling pathway has been exploited as a promising strategy in treating these diseases. However, most studies only focused on the design of allosteric antagonists-bound receptors on the intracellular side of IL-8 receptors. Recently, the first cryo-EM structures of IL-8-CXCR2-Gi complexes have been solved, revealing the unique binding and activation modes of the endogenous chemokine IL-8. Hence, we set to identify small molecule inhibitors for IL-8 using critical protein-protein interaction between IL-8 and CXCR2 at the orthosteric binding site. The pharmacophore models and molecular docking screened compounds from DrugBank and NCI databases. The oral bioavailability of the top 23 ligands from the screening was then predicted by the SwissAMDE tool. Molecular dynamics simulation and free binding energy calculation were performed for the best compounds. The result indicated that DB14770, DB12121, and DB03916 could form strong interactions and stable protein-ligand complexes with IL-8. These three candidates are potential IL-8 inhibitors that can be further evaluated by in vitro experiments in the next stage.

The reorganization energy of compounds upon binding to proteins, from dynamic and solvated bound and unbound states

The intramolecular reorganization energy (ΔE_Reorg) of compounds upon binding to proteins is a component of the binding free energy, which has long received particular attention, for fundamental and practical reasons. Understanding ΔE_Reorg would benefit the science of molecular recognition and drug design. For instance, the tolerable strain energy of compounds upon binding has been elusive. Prior studies found some large ΔE_Reorg values (e.g. > 10 kcal/mol), received with skepticism since they imply excessive opposition to binding. Indeed, estimating ΔE_Reorg is technically difficult. Typically, ΔE_Reorg has been approached by taking two energy-minimized conformers representing the bound and unbound states, and subtracting their conformational energy. This is a drastic oversimplification, liable to conformational collapse of the unbound conformer. Instead, the present work applies extensive molecular dynamics (MD) and the modern OPLS3 force-field to simulate compounds bound and unbound states, in explicit solvent under physically relevant conditions. The thermalized unbound compounds populate multiple conformations, not reducible to one or a few energy-minimized conformers. The intramolecular energies in the bound and unbound states were averaged over pertinent conformational ensembles, and the reorganization enthalpy upon binding (ΔH_Reorg) deduced by subtraction. This was applied to 76 systems, including 43 approved drugs, carefully selected for i) the quality of the bioactive X-ray structures and ii) the diversity of the chemotypes, their properties and protein targets. It yielded comparatively low ΔH_Reorg values (median = 1.4 kcal/mol, mean = 3.0 kcal/mol). A new finding is the observation of negative ΔH_Reorg values. Indeed, reorganization energies do not have to oppose binding, e.g. when intramolecular interactions stabilize preferentially the bound state. Conversely, even with competing water molecules, intramolecular interactions can occur predominantly in the unbound compound, and be replaced by intermolecular counterparts upon protein binding. Such disruption of intramolecular interactions upon binding gives rise to occasional larger ΔH_Reorg values. Such counterintuitive larger ΔH_Reorg values may be rationalized as a redistribution of interactions upon binding, qualitatively compatible with binding.

Molecular docking and pharmacophore models to probe binding hypothesis of inhibitors of hypoxia inducible factor-1

Hypoxia inducible factor-1 is a heterodimeric transcription factor that regulates cellular responses to hypoxia and is involved in tumor progression and resistance to chemotherapy. Dimerization between HIF-1α and β subunits has been recognized crucial for DNA binding and transcriptional activity of HIF-1. Therefore, inhibitors of α and β dimerization subunits of HIF-1 may potentially evade HIF-1-mediated chemotherapy resistance. In the current study, ligand-based pharmacophore model was developed to determine 3 D binding features of HIF-1 inhibitors. The selected pharmacophore model comprises of one hydrogen bond donor, one hydrogen bond acceptor and one hydrophobic feature. The selected model was used for virtual screening of publically available data base by ChemBridge Corporation. Overall, six potential hits against HIF-1α and β dimerization have been identified. These include, Hit 1 (4-(4-chlorophenyl)-2,6-dimethyl-3,5-pyridinedicarboxylic acid), 3 (2-[2-(2-hydroxybenzoyl)carbonohydrazonoyl]benzoic acid) and 5 (3-(4-methoxyphenyl)-2,4-quinolinedicarboxylic acid) nicotonic acid derivatives, Hit 2 (3-[(1-adamantylamino)sulfonyl]benzoic acid), 4 (5-{[(2-fluorophenyl)amino]sulfonyl}-2-methylbenzoic acid), and 6 (4-({[2-(trifluoromethyl)phenyl]sulfonyl}amino)benzoic acid) sulfonamide derivatives. Additionally, adamantyl moiety of compound 2 shows interactions with the experimentally known hydrophobic amino acid residues (V336, C334, E245) of HIF-1α and β dimerization site. The identified hits showed lower to higher µM biological activity (IC₅₀) values and thus, after further structure optimization may serve as potential inhibitor of HIF-1 dimerization in cancer chemotherapy.Communicated by Ramaswamy H. Sarma.

AOA-2 Derivatives as Outer Membrane Protein A Inhibitors for Treatment of Gram-Negative Bacilli Infections

Previously, we identified that a cyclic hexapeptide AOA-2 inhibited the interaction of Gram-negative bacilli (GNB) like Acinetobacter baumannii, Pseudomonas aeruginosa, and Escherichia coli to host cells thereby preventing the development of infection in vitro and in a murine sepsis peritoneal model. In this work, we aimed to evaluate in vitro a library of AOA-2 derivatives in order to improve the effect of AOA-2 against GNB infections. Ten AOA-2 derivatives were synthetized for the in vitro assays. Their toxicities to human lung epithelial cells (A549 cells) for 24 h were evaluated by determining the A549 cells viability using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. The effect of these peptide derivatives and AOA-2 at 250, 125, 62.5, and 31.25 μg/mL on the attachment of A. baumannii ATCC 17978, P. aeruginosa PAO1 and E. coli ATCC 25922 strains to A549 cells was characterized by adherence and viability assays. None of the 10 derivatives showed toxicity to A549 cells. RW01 and RW06 have reduced more the adherence of ATCC 17978, PAO1 and ATCC 2599 strains to A549 cells when compared with the original compound AOA-2. Moreover, both peptides have increased slightly the viability of infected A549 cells by PAO1 and ATCC 25922 than those observed with AOA-2. Finally, RW01 and RW06 have potentiated the activity of colistin against ATCC 17978 strain in the same level with AOA-2. The optimization program of AOA-2 has generated two derivatives (RW01 and RW06) with best effect against interaction of GNB with host cells, specifically against P. aeruginosa and E. coli.

An Overview of Antiretroviral Agents for Treating HIV Infection in Paediatric Population

Paediatric Acquired ImmunoDeficiency Syndrome (AIDS) is a life-threatening and infectious disease in which the Human Immunodeficiency Virus (HIV) is mainly transmitted through Mother-To- Child Transmission (MTCT) during pregnancy, labour and delivery, or breastfeeding. This review provides an overview of the distinct therapeutic alternatives to abolish the systemic viral replication in paediatric HIV-1 infection. Numerous classes of antiretroviral agents have emerged as therapeutic tools for downregulation of different steps in the HIV replication process. These classes encompass Non- Nucleoside Analogue Reverse Transcriptase Inhibitors (NNRTIs), Nucleoside/Nucleotide Analogue Reverse Transcriptase Inhibitors (NRTIs/NtRTIs), INtegrase Inhibitors (INIs), Protease Inhibitors (PIs), and Entry Inhibitors (EIs). Co-administration of certain antiretroviral drugs with Pharmacokinetic Enhancers (PEs) may boost the effectiveness of the primary therapeutic agent. The combination of multiple antiretroviral drug regimens (Highly Active AntiRetroviral Therapy - HAART) is currently the standard therapeutic approach for HIV infection. So far, the use of HAART offers the best opportunity for prolonged and maximal viral suppression, and preservation of the immune system upon HIV infection. Still, the frequent administration of high doses of multiple drugs, their inefficient ability to reach the viral reservoirs in adequate doses, the development of drug resistance, and the lack of patient compliance compromise the complete HIV elimination. The development of nanotechnology-based drug delivery systems may enable targeted delivery of antiretroviral agents to inaccessible viral reservoir sites at therapeutic concentrations. In addition, the application of Computer-Aided Drug Design (CADD) approaches has provided valuable tools for the development of anti-HIV drug candidates with favourable pharmacodynamics and pharmacokinetic properties.

DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance

The progesterone receptor (PR) is important therapeutic target for many malignancies and endocrine disorders due to its role in controlling ovulation and pregnancy via the reproductive cycle. Therefore, the modulation of PR activity using its agonists and antagonists is receiving increasing interest as novel treatment strategy. However, clinical trials using the PR modulators have not yet been found conclusive evidences. Recently, increasing evidence from several fields shows that the classification of chemical compounds, including agonists and antagonists, can be done with recent improvements in deep learning (DL) using deep neural network. Therefore, we recently proposed a novel DL-based quantitative structure-activity relationship (QSAR) strategy using transfer learning to build prediction models for agonists and antagonists. By employing this novel approach, referred as DeepSnap-DL method, which uses images captured from 3-dimension (3D) chemical structure with multiple angles as input data into the DL classification, we constructed prediction models of the PR antagonists in this study. Here, the DeepSnap-DL method showed a high performance prediction of the PR antagonists by optimization of some parameters and image adjustment from 3D-structures. Furthermore, comparison of the prediction models from this approach with conventional machine learnings (MLs) indicated the DeepSnap-DL method outperformed these MLs. Therefore, the models predicted by DeepSnap-DL would be powerful tool for not only QSAR field in predicting physiological and agonist/antagonist activities, toxicity, and molecular bindings; but also for identifying biological or pathological phenomena.

Conformational analysis of macrocycles: comparing general and specialized methods

Abstract

Macrocycles represent an important class of medicinally relevant small molecules due to their interesting biological properties. Therefore, a firm understanding of their conformational preferences is important for drug design. Given the importance of macrocycle-protein modelling in drug discovery, we envisaged that a systematic study of both classical and recent specialized methods would provide guidance for other practitioners within the field. In this study we compare the performance of the general, well established conformational analysis methods Monte Carlo Multiple Minimum (MCMM) and Mixed Torsional/Low-Mode sampling (MTLMOD) with two more recent and specialized macrocycle sampling techniques: MacroModel macrocycle Baseline Search (MD/LLMOD) and Prime macrocycle conformational sampling (PRIME-MCS). Using macrocycles extracted from 44 macrocycle-protein X-ray crystallography complexes, we evaluated each method based on their ability to (i) generate unique conformers, (ii) generate unique macrocycle ring conformations, (iii) identify the global energy minimum, (iv) identify conformers similar to the X-ray ligand conformation after Protein Preparation Wizard treatment (X-ray_ppw), and (v) to the X-ray_ppw ring conformation. Computational speed was also considered. In addition, conformational coverage, as defined by the number of conformations identified, was studied. In order to study the relative energies of the bioactive conformations, the energy differences between the global energy minima and the energy minimized X-ray_ppw structures and, the global energy minima and the MCMM-Exhaustive (1,000,000 search steps) generated conformers closest to the X-ray_ppw structure, were calculated and analysed. All searches were performed using relatively short run times (10,000 steps for MCMM, MTLMOD and MD/LLMOD). To assess the performance of the methods, they were compared to an exhaustive MCMM search using 1,000,000 search steps for each of the 44 macrocycles (requiring ca 200 times more CPU time). Prior to our analysis, we also investigated if the general search methods MCMM and MTLMOD could also be optimized for macrocycle conformational sampling. Taken together, our work concludes that the more general methods can be optimized for macrocycle modelling by slightly adjusting the settings around the ring closure bond. In most cases, MCMM and MTLMOD with either standard or enhanced settings performed well in comparison to the more specialized macrocycle sampling methods MD/LLMOD and PRIME-MCS. When using enhanced settings for MCMM and MTLMOD, the X-ray_ppw conformation was regenerated with the greatest accuracy. The, MD/LLMOD emerged as the most efficient method for generating the global energy minima.

Graphic abstract

Electronic supplementary material

The online version of this article (10.1007/s10822-020-00277-2) contains supplementary material, which is available to authorized users.

Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library

The constitutive androstane receptor (CAR) plays pivotal roles in drug-induced liver injury through the transcriptional regulation of drug-metabolizing enzymes and transporters. Thus, identifying regulatory factors for CAR activation is important for understanding its mechanisms. Numerous studies conducted previously on CAR activation and its toxicity focused on in vivo or in vitro analyses, which are expensive, time consuming, and require many animals. We developed a computational model that predicts agonists for the CAR using the Toxicology in the 21st Century 10k library. Additionally, we evaluate the prediction performance of novel deep learning (DL)-based quantitative structure-activity relationship analysis called the DeepSnap-DL approach, which is a procedure of generating an omnidirectional snapshot portraying three-dimensional (3D) structures of chemical compounds. The CAR prediction model, which applies a 3D structure generator tool, called CORINA-generated and -optimized chemical structures, in the DeepSnap-DL demonstrated better performance than the existing methods using molecular descriptors. These results indicate that high performance in the prediction model using the DeepSnap-DL approach may be important to prepare suitable 3D chemical structures as input data and to enable the identification of modulators of the CAR.

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR) Analysis

Numerous chemical compounds are distributed around the world and may affect the homeostasis of the endocrine system by disrupting the normal functions of hormone receptors. Although the risks associated with these compounds have been evaluated by acute toxicity testing in mammalian models, the chronic toxicity of many chemicals remains due to high cost of the compounds and the testing, etc. However, computational approaches may be promising alternatives and reduce these evaluations. Recently, deep learning (DL) has been shown to be promising prediction models with high accuracy for recognition of images, speech, signals, and videos since it greatly benefits from large datasets. Recently, a novel DL-based technique called DeepSnap was developed to conduct QSAR analysis using three-dimensional images of chemical structures. It can be used to predict the potential toxicity of many different chemicals to various receptors without extraction of descriptors. DeepSnap has been shown to have a very high capacity in tests using Tox21 quantitative qHTP datasets. Numerous parameters must be adjusted to use the DeepSnap method but they have not been optimized. In this study, the effects of these parameters on the performance of the DL prediction model were evaluated in terms of the loss in validation as an indicator for evaluating the performance of the DL using the toxicity information in the Tox21 qHTP database. The relations of the parameters of DeepSnap such as (1) number of molecules per SDF split into (2) zoom factor percentage, (3) atom size for van der waals percentage, (4) bond radius, (5) minimum bond distance, and (6) bond tolerance, with the validation loss following quadratic function curves, which suggests that optimal thresholds exist to attain the best performance with these prediction models. Using the parameter values set with the best performance, the prediction model of chemical compounds for CAR agonist was built using 64 images, at 105° angle, with AUC of 0.791. Thus, based on these parameters, the proposed DeepSnap-DL approach will be highly reliable and beneficial to establish models to assess the risk associated with various chemicals.

Designing modified polybrominated diphenyl ether BDE-47, BDE-99, BDE-100, BDE-183, and BDE-209 molecules with decreased estrogenic activities using 3D-QSAR, pharmacophore models coupled with resolution V of the 210-3 fractional factorial design and molecular docking

A 3D-QSAR model was constructed to predict polybrominated diphenyl ether (PBDE) estrogenic activities expressed as median effective concentrations (pEC₅₀), and resolution V of the 2^10-3 fractional factorial design and a pharmacophore model were used to modify the target PBDE molecules BDE-47, BDE-99, BDE-100, BDE-183, and BDE-209 to decrease the estrogenic activities. The persistent-organic-pollutant-related and flame-retardant properties of the modified molecules were evaluated. The mechanisms involved in decreasing PBDE estrogenic activities were explored through molecular docking. The 3D-QSAR model gave a cross-validated correlation coefficient (q²) of 0.682 (i.e., >0.5) and a non-cross-validated correlation coefficient (r²) of 0.980 (i.e., >0.9). Mono- and di-substitutions and hydrophobic substituent groups gave 40 modified molecules with decreased estrogenic activities, including modified BDE-47 and BDE-99 with pEC₅₀ decreased by >10% and modified BDE-100, BDE-183, and BDE-209 with pEC₅₀ decreased by >20%. The modified molecules had similar flame-retardancy to the unmodified molecules, and lower biotoxicities (by a maximum of 17.27%), persistences (by a maximum of 55.68%), bioconcentration (by 4.28%-23.91%), and long-range transport potentials (by 0.72%-18.47%). Docking indicated that hydrophobic interactions were the main factors affecting PBDE estrogenic activities. The results provide a theoretical basis for designing less estrogenic flame retardants than are currently available.

Conformator: A Novel Method for the Generation of Conformer Ensembles

Computer-aided drug design methods such as docking, pharmacophore searching, 3D database searching, and the creation of 3D-QSAR models need conformational ensembles to handle the flexibility of small molecules. Here, we present Conformator, an accurate and effective knowledge-based algorithm for generating conformer ensembles. With 99.9% of all test molecules processed, Conformator stands out by its robustness with respect to input formats, molecular geometries, and the handling of macrocycles. With an extended set of rules for sampling torsion angles, a novel algorithm for macrocycle conformer generation, and a new clustering algorithm for the assembly of conformer ensembles, Conformator reaches a median minimum root-mean-square deviation (measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers) of 0.47 Å with no significant difference to the highest-ranked commercial algorithm OMEGA and significantly higher accuracy than seven free algorithms, including the RDKit DG algorithm. Conformator is freely available for noncommercial use and academic research.

Energy windows for computed compound conformers: covering artefacts or truly large reorganization energies?

The generation of 3D conformers of small molecules underpins most computational drug discovery. Thus, the conformer quality is critical and depends on their energetics. A key parameter is the empirical conformational energy window (ΔEw), since only conformers within ΔEw are retained. However, ΔEw values in use appear unrealistically large. We analyze the factors pertaining to the conformer energetics and ΔEw. We argue that more attention must be focused on the problem of collapsed low-energy conformers. That is due to artificial intramolecular stabilization and occurs even with continuum solvation. Consequently, the conformational energy of extended bioactive structures is artefactually increased, which inflates ΔEw. Thus, this Perspective highlights the issues arising from low-energy conformers and suggests improvements via empirical or physics-based strategies.

Drug target prediction using chem- and bioinformatics

The biological pre-validation of natural products (NPs) and their underlying frameworks ensures an unrivaled source of inspiration for chemical probe and drug design. However, the poor knowledge of their drug target counterparts critically hinders the broader exploration of NPs in chemical biology and molecular medicine. Cutting-edge algorithms now provide powerful means for the target deconvolution of phenotypic screen hits and generate motivated research hypotheses. Herein, we present recent progress in artificial intelligence applied to target identification that may accelerate future NP-inspired molecular medicine.

Pharmacophore modeling for identification of anti-IGF-1R drugs and in-vitro validation of fulvestrant as a potential inhibitor

Insulin-like growth factor 1 receptor (IGF-1R) is an important therapeutic target for breast cancer treatment. The alteration in the IGF-1R associated signaling network due to various genetic and environmental factors leads the system towards metastasis. The pharmacophore modeling and logical approaches have been applied to analyze the behaviour of complex regulatory network involved in breast cancer. A total of 23 inhibitors were selected to generate ligand based pharmacophore using the tool, Molecular Operating Environment (MOE). The best model consisted of three pharmacophore features: aromatic hydrophobic (HyD/Aro), hydrophobic (HyD) and hydrogen bond acceptor (HBA). This model was validated against World drug bank (WDB) database screening to identify 189 hits with the required pharmacophore features and was further screened by using Lipinski positive compounds. Finally, the most effective drug, fulvestrant, was selected. Fulvestrant is a selective estrogen receptor down regulator (SERD). This inhibitor was further studied by using both in-silico and in-vitro approaches that showed the targeted effect of fulvestrant in ER+ MCF-7 cells. Results suggested that fulvestrant has selective cytotoxic effect and a dose dependent response on IRS-1, IGF-1R, PDZK1 and ER-α in MCF-7 cells. PDZK1 can be an important inhibitory target using fulvestrant because it directly regulates IGF-1R.

Synthesis and characterization of water-soluble macrocyclic peptides stabilizing protein α-turn

Macrocyclic peptides mimic tight “non-classical” α-turn type II-α_LS found in proteins, as shown by spectroscopic and computational analysis of their equilibrating conformations.

Benchmarking Commercial Conformer Ensemble Generators

We assess and compare the performance of eight commercial conformer ensemble generators (ConfGen, ConfGenX, cxcalc, iCon, MOE LowModeMD, MOE Stochastic, MOE Conformation Import, and OMEGA) and one leading free algorithm, the distance geometry algorithm implemented in RDKit. The comparative study is based on a new version of the Platinum Diverse Dataset, a high-quality benchmarking dataset of 2859 protein-bound ligand conformations extracted from the PDB. Differences in the performance of commercial algorithms are much smaller than those observed for free algorithms in our previous study (J. Chem. Inf.

MODEL

2017, 57, 529-539). For commercial algorithms, the median minimum root-mean-square deviations measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers are between 0.46 and 0.61 Å. Commercial conformer ensemble generators are characterized by their high robustness, with at least 99% of all input molecules successfully processed and few or even no substantial geometrical errors detectable in their output conformations. The RDKit distance geometry algorithm (with minimization enabled) appears to be a good free alternative since its performance is comparable to that of the midranked commercial algorithms. Based on a statistical analysis, we elaborate on which algorithms to use and how to parametrize them for best performance in different application scenarios.

Conformational energy range of ligands in protein crystal structures: The difficult quest for accurate understanding

In this review, we address a fundamental question: What is the range of conformational energies seen in ligands in protein-ligand crystal structures? This value is important biophysically, for better understanding the protein-ligand binding process; and practically, for providing a parameter to be used in many computational drug design methods such as docking and pharmacophore searches. We synthesize a selection of previously reported conflicting results from computational studies of this issue and conclude that high ligand conformational energies really are present in some crystal structures. The main source of disagreement between different analyses appears to be due to divergent treatments of electrostatics and solvation. At the same time, however, for many ligands, a high conformational energy is in error, due to either crystal structure inaccuracies or incorrect determination of the reference state. Aside from simple chemistry mistakes, we argue that crystal structure error may mainly be because of the heuristic weighting of ligand stereochemical restraints relative to the fit of the structure to the electron density. This problem cannot be fixed with improvements to electron density fitting or with simple ligand geometry checks, though better metrics are needed for evaluating ligand and binding site chemistry in addition to geometry during structure refinement. The ultimate solution for accurately determining ligand conformational energies lies in ultrahigh-resolution crystal structures that can be refined without restraints.

Searching for bioactive conformations of drug-like ligands with current force fields: how good are we?

Drug-like ligands obtained from protein-ligand complexes deposited in the Protein Databank were subjected to conformational searching using various force fields and solvation settings. For each ligand, the resulting conformer pool was examined for the presence of the bioactive (crystal pose-like) conformation. Similarity of conformers toward the crystal-pose was quantified as the best achievable root mean squared deviation (RMSD, heavy atoms only). Analyzing the conformer pools generated by various force fields revealed only small differences in the likelihood of finding a crystal pose-like conformation. However, employing different solvents in the conformational search was found to be very important for achieving RMSDs below 1.0 Å. The best statistical values of likelihood were observed with a recently released force field covering a large portion of dihedral angles occurring in drug-like compounds in combination with the water as solvent. In order to enable computational chemists and modelers to efficiently use available software tools, we have additionally performed several focused analyses on ligands, grouped according to descriptors most relevant for the rational drug design.

High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators

We developed a cheminformatics pipeline for the fully automated selection and extraction of high-quality protein-bound ligand conformations from X-ray structural data. The pipeline evaluates the validity and accuracy of the 3D structures of small molecules according to multiple criteria, including their fit to the electron density and their physicochemical and structural properties. Using this approach, we compiled two high-quality datasets from the Protein Data Bank (PDB): a comprehensive dataset and a diversified subset of 4626 and 2912 structures, respectively. The datasets were applied to benchmarking seven freely available conformer ensemble generators: Balloon (two different algorithms), the RDKit standard conformer ensemble generator, the Experimental-Torsion basic Knowledge Distance Geometry (ETKDG) algorithm, Confab, Frog2 and Multiconf-DOCK. Substantial differences in the performance of the individual algorithms were observed, with RDKit and ETKDG generally achieving a favorable balance of accuracy, ensemble size and runtime. The Platinum datasets are available for download from http://www.zbh.uni-hamburg.de/platinum_dataset .

Analysis of positions and substituents on genotoxicity of fluoroquinolones with quantitative structure-activity relationship and 3D Pharmacophore model

The genotoxicity values of 21 quinolones were studied to establish a quantitative structure-activity relationship model and 3D Pharmacophore model separately for screening essential positions and substituents that contribute to genotoxicity of fluoroquinolones (FQs). A full factor experimental design was performed to analyze the specific main effect and second-order interaction effect of different positions and substituents on genotoxicity, forming a reasonable modification scheme which was validated on typical FQ with genotoxicity and efficacy data. Four positions (1, 5, 7, 8) were screened finally to form the full factorial experimental design which contained 72 congeners in total, illustrating that: the dominant effect of 5 and 7-positions on genotoxicity of FQs is main effect; meanwhile the effect of 1 and 8-positions is a second-order interaction effect; two adjacent positions always have stronger second-order interaction effect and lower genotoxicity; the obtained modification scheme had been validated on typical FQ congeners with the modified compound has a lower genotoxicity, higher synthesis feasibilities and efficacy.

Design, synthesis, molecular modeling and anti-hyperglycemic evaluation of novel quinoxaline derivatives as potential PPARγ and SUR agonists

In our effort to develop potent anti-hyperglycemic agents with potential agonistic activities toward PPARγ and SUR, three novel series of quinoxaline derivatives bearing sulfonylurea or sulfonylthiourea moieties with different linkers were designed and synthesized. Some of the newly synthesized compounds were evaluated in vivo for their anti-hyperglycemic activities in STZ-induced hyperglycemic rats. Compounds 15_a, 15_e, 19_b and 24_a exhibited the highest anti-hyperglycemic activities with % reduction in blood glucose level of (50.58, 43.84, 45.10 and 49.62, respectively). Additionally, eight compounds revealed potent anti-hyperglycemic activities were further evaluated in vitro for their PPARγ binding affinity and insulin-secreting ability as potential mechanisms for anti-hyperglycemic activity. Four compounds (15_a, 15_b, 15_d and 15_e) significantly bound to PPARγ with IC₅₀ values of 0.482, 0.491, 0.350 and 0.369μM, respectively. Moreover, Compounds 15_a and 15_b have demonstrated induction of insulin-secretion with EC₅₀ values of 0.92 and 0.98μM, respectively. Furthermore, molecular docking and pharmacophore generation techniques were carried out to investigate binding patterns and fit values of the designed compounds with PPARγ and SUR, respectively.

Medicinal Chemistry Strategies to Disrupt the p53-MDM2/MDMX Interaction

The growth inhibitory activity of p53 tumor suppressor is tightly regulated by interaction with two negative regulatory proteins, murine double minute 2 (MDM2) and X (MDMX), which are overexpressed in about half of all human tumors. The elucidation of crystallographic structures of MDM2/MDMX complexes with p53 has been pivotal for the identification of several classes of inhibitors of the p53-MDM2/MDMX interaction. The present review provides in silico strategies and screening approaches used in drug discovery as well as an overview of the most relevant classes of small-molecule inhibitors of the p53-MDM2/MDMX interaction, their progress in pipeline, and highlights particularities of each class of inhibitors. Most of the progress made with high-throughput screening has led to the development of inhibitors belonging to the cis-imidazoline, piperidinone, and spiro-oxindole series. However, novel potent and selective classes of inhibitors of the p53-MDM2 interaction with promising antitumor activity are emerging. Even with the discovery of the 3D structure of complex p53-MDMX, only two small molecules were reported as selective p53-MDMX antagonists, WK298 and SJ-172550. Dual inhibition of the p53-MDM2/MDMX interaction has shown to be an alternative approach since it results in full activation of the p53-dependent pathway. The knowledge of structural requirements crucial to the development of small-molecule inhibitors of the p53-MDMs interactions has enabled the identification of novel antitumor agents with improved in vivo efficacy.

Dipole moments of trans- and cis-(4-methylcyclohexyl)methanol (4-MCHM): obtaining the right conformer for the right reason

Accurate computational estimates of fundamental physical properties can be used as inputs in the myriad of extant models employed to predict toxicity, transport, and fate of contaminants. However, as molecular complexity of contaminants increases, it becomes increasingly difficult to determine the magnitude of the errors introduced by ignoring the 3D conformational space averaging within group-additivity and semi-empirical approaches. The importance of considering 3D molecular structure is exemplified for the dipole moments of cis and trans isomers of (4-methylcyclohexyl)methanol (4-MCHM). When 10 000 gallons of 4-MCHM was spilled into the Elk River in January 2014, a lack of toxicological data and environmental partitioning coefficients hindered the immediate protection of human health and the local water supply in West Virginia, USA. Post-spill analysis of the contaminants suggested that the cis and trans isomers had observably different partitioning coefficients and solubility, and thus differing environmental fates. Obtaining high-quality dipole moments using ab initio quantum chemical methods for the isomeric pair was crucial in validating their experimental differences in solubility [Environ. Sci. Technol. Lett., 2015, 2, 127]. The use of first principles electronic structure theory is further explored here to obtain accurate conformer relative energies and dipole moments of cis- and trans-4-MCHM. Overall, the MP2 aug-cc-pVDZ level of theory affords the best balance between accuracy and computational cost.

Modification of PBDEs (BDE-15, BDE-47, BDE-85 and BDE-126) biological toxicity, bio-concentration, persistence and atmospheric long-range transport potential based on the pharmacophore modeling assistant with the full factor experimental design

In this study, the properties of AhR binding affinity, bio-concentration factor, half-life and vapor pressure were selected as the typical indicators of biological toxicity, bio-concentration, persistence and atmospheric long-range transport potential for polybrominated diphenyl ethers (PBDEs), respectively. A three-dimensional pharmacophore modeling assistant with a full factor experimental design for each property was used to reveal the significant pharmacophore features and the substituent effects to obtain reasonable modified schemes for the selected target PBDEs. Finally, the performances of the persistent organic pollutant (POP) properties, the synthesis feasibility and the fire resistance of the modified compounds were evaluated. The most influential pharmacophore feature for all POP properties was the hydrophobic group, especially the vinyl and propyl groups. Modified compounds with two additional hydrophobic groups exhibited a better regulatory performance. The average reduction in the proportions of the four POP properties for the modified compounds (except for 3-phenyl-BDE-15) was 70.60%, 52.44%, 47.04% and 70.88%. In addition, the energy and the C-Br bond dissociation enthalpy of the four typical PBDEs were higher than those of the modified compounds (except for 3-phenyl-BDE-15), indicating the synthesis feasibility and the lower energy barrier of the modified compounds to release Br free radicals to provide fire resistance.

Towards understanding the unbound state of drug compounds: Implications for the intramolecular reorganization energy upon binding

There has been an explosion of structural information for pharmaceutical compounds bound to biological targets, but the conformations and dynamics of compounds free in solution are poorly characterized, if at all. Yet, knowledge of the unbound state is essential to understand the fundamentals of molecular recognition, including the much debated conformational intramolecular reorganization energy of a compound upon binding (ΔEReorg). Also, dependable observation of the unbound compounds is important for ligand-based drug discovery, e.g. with pharmacophore modelling. Here, these questions are addressed with long (⩾0.5μs) state-of-the-art molecular dynamics (MD) simulations of 26 compounds (including 7 approved drugs) unbound in explicit solvent. These compounds were selected to be chemically diverse, with a range of flexibility, and good quality bioactive X-ray structures. The MD-simulated free compounds are compared to their bioactive structure and conformers generated with ad hoc sampling in vacuo or with implicit generalized Born (GB) aqueous solvation models. The GB conformational models clearly depart from those obtained in explicit solvent, and suffer from conformational collapse almost as severe as in vacuo. Thus, the global energy minima in vacuo or with GB are not suitable representations of the unbound state, which can instead be extensively sampled by MD simulations. Many, but not all, MD-simulated compounds displayed some structural similarity to their bioactive structure, supporting the notion of conformational pre-organization for binding. The ligand-protein complexes were also simulated in explicit solvent, to estimate ΔEReorg as an enthalpic difference ΔHReorg between the intramolecular energies in the bound and unbound states. This fresh approach yielded ΔHReorg values⩽6kcal/mol for 18 out of 26 compounds. For three particularly polar compounds 15⩽ΔHReorg⩽20kcal/mol, supporting the notion that ΔHReorg can be substantial. Those large ΔHReorg values correspond to a redistribution of electrostatic interactions upon binding. Overall, the study illustrates how MD simulations offer a promising avenue to characterize the unbound state of medicinal compounds.

Identification of Structurally Diverse Antimicrobials Through Sequential Application of Pharmacophore Modeling, Virtual Screening, Molecular Docking and In Vitro Microbiological Assay

Dihydrofolate reductase enzyme has been an attractive biological target for the design and development of antimicrobials. Considering this, we have attempted to identify novel dihydrofolate reductase inhibitors through our well-defined in silico and in vitro work flow. An accurate and predictive pharmacophore model comprising of one hydrogen bond acceptor, two hydrophobic and one ring aromatic was developed and utilized as a query to search the National Cancer Institute and Maybridge database leading to retrieval of various compounds which were filtered on the basis of estimated activity, fit value and Lipinski's violation. Selected hits NSC3423, KM09759, NSC391, NSC2091 and HTS00630 were subjected to docking studies which resulted into visualization of potential interaction capabilities of hits in line to pharmacophoric features. The identified hits were evaluated for in vitro antimicrobial potential, and the results revealed that among all the five hits, NSC3423 is the most potent compound with activity against E. coli, P. aeruginosa, S. aureus, B. substilis, A. niger and F. oxysporum. On the other hand, KM09759, NSC391, NSC2091 and HTS00630 showed varying degree of activities against gram-positive, gram-negative and fungal strains.

Free Energy-Based Conformational Search Algorithm Using the Movable Type Sampling Method

In this article, we extend the movable type (MT) sampling method to molecular conformational searches (MT-CS) on the free energy surface of the molecule in question. Differing from traditional systematic and stochastic searching algorithms, this method uses Boltzmann energy information to facilitate the selection of the best conformations. The generated ensembles provided good coverage of the available conformational space including available crystal structures. Furthermore, our approach directly provides the solvation free energies and the relative gas and aqueous phase free energies for all generated conformers. The method is validated by a thorough analysis of thrombin ligands as well as against structures extracted from both the Protein Data Bank (PDB) and the Cambridge Structural Database (CSD). An in-depth comparison between OMEGA and MT-CS is presented to illustrate the differences between the two conformational searching strategies, i.e., energy-based versus free energy-based searching. These studies demonstrate that our MT-based ligand conformational search algorithm is a powerful approach to delineate the conformational ensembles of molecular species on free energy surfaces.

BCL::Conf: small molecule conformational sampling using a knowledge based rotamer library

The interaction of a small molecule with a protein target depends on its ability to adopt a three-dimensional structure that is complementary. Therefore, complete and rapid prediction of the conformational space a small molecule can sample is critical for both structure- and ligand-based drug discovery algorithms such as small molecule docking or three-dimensional quantitative structure–activity relationships. Here we have derived a database of small molecule fragments frequently sampled in experimental structures within the Cambridge Structure Database and the Protein Data Bank. Likely conformations of these fragments are stored as ‘rotamers’ in analogy to amino acid side chain rotamer libraries used for rapid sampling of protein conformational space. Explicit fragments take into account correlations between multiple torsion bonds and effect of substituents on torsional profiles. A conformational ensemble for small molecules can then be generated by recombining fragment rotamers with a Monte Carlo search strategy. BCL::Conf was benchmarked against other conformer generator methods including Confgen, Moe, Omega and RDKit in its ability to recover experimentally determined protein bound conformations of small molecules, diversity of conformational ensembles, and sampling rate. BCL::Conf recovers at least one conformation with a root mean square deviation of 2 Å or better to the experimental structure for 99 % of the small molecules in the Vernalis benchmark dataset. The ‘rotamer’ approach will allow integration of BCL::Conf into respective computational biology programs such as Rosetta.Graphical abstract:

Discovery of a selective, safe and novel anti-malarial compound with activity against chloroquine resistant strain of Plasmodium falciparum

In recent years the DNA minor groove has attracted much attention for the development of anti-malarial agents. In view of this we have attempted to discover novel DNA minor groove binders through in-silico and in-vitro workflow. A rigorously validated pharmacophore model comprising of two positive ionizable (PI), one hydrophobic (HY) and one ring aromatic (RA) features was used to mine NCI chemical compound database. This led to retrieval of many hits which were screened on the basis of estimated activity, fit value and Lipinski's violation. Finally two compounds NSC639017 and NSC371488 were evaluated for their in-vitro anti-malarial activities against Plasmodium falciparum 3D7 (CQ sensitive) and K1 (CQ resistant) strains by SYBR green-I based fluorescence assay. The results revealed that out of two, NSC639017 posses excellent anti-malarial activity particularly against chloroquine resistant strain and moreover NSC639017 also appeared to be safe (CC50 126.04 μg/ml) and selective during cytotoxicity evaluation.