Three-dimensional pharmacophore methods in drug discovery

The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Hit to Lead

The Hit to Lead (H2L) process is an integral part of contemporary drug discovery, encompassing the optimisation of validated Hit structures into Lead molecules. High quality leads build confidence, through activity and property profiles as well as preliminary biological data, which might include validating pharmacologic hypotheses along the way, indicating that further investment in the structure(s) and target would be worthwhile. Leads have line of sight to a development candidate and bring an understanding of what priorities Lead Optimisation should address. In this set of best practices, we detail the essential criteria that characterise a good lead, which include establishing SAR from analogues and assessing preliminary DMPK indicators, selectivity and early safety parameters. We highlight the importance of identifying liabilities of the lead series and demonstrating that each can be individually modulated whilst maintaining on target potency. We make the case for having physicochemical properties as critical optimisation parameters and how ligand efficiency metrics can enable this. Then we go over general tactics that can be used to convert hits into a lead series. These include essential steps that, when performed early, increase the chance of success such as deconstructive SAR, pharmacophore and bioactive conformation determination and scaffold optimisation. Finally, we suggest decision-making criteria to substantiate confidence in further investment or, as importantly, making a recommendation to cease further work on a series.

Molecular modelling and experimental validation of mangiferin and its related compounds as quorum sensing modulators of Pseudomonas aeruginosa

The LasR quorum sensing system regulates the virulence factors of Pseudomonas aeruginosa, a multi-drug resistant pathogen. Mangiferin and related compounds have been found to modulate this system as determined by in silico and in vitro experimental procedures. ZINCPharmer was used to compile a library of over 1000 metabolites that were screened to the top five based on shared pharmacophores and drug-like properties with mangiferin. Molecular docking and molecular dynamics simulation (140 ns) showed that ZINC E (- 55.64 ± 2.93 kcal/mol) and ZINC D (- 54.51 ± 2.82 kcal/mol) had significantly lower binding free energy compared to mangiferin-LasR (- 42.24 ± 3.94 kcal/mol) and the reference standard (azithromycin-LasR (- 40.01 ± 6.15 kcal/mol). ZINC D (95.16%) competed favorably with mangiferin (95.77%) as potential QS modulators at sub-minimum inhibitory concentrations relative to ZINC E (85.07%) and azithromycin (85.79%). These observations suggest mangiferin and related lead compounds as potential drug candidates for P. aeruginosa infection management.

Discovery of novel A2AR antagonist via 3D-QSAR pharmacophore modeling: neuroprotective effects in 6-OHDA-induced SH-SY5Y cells and haloperidol-induced Parkinsonism in C57 bl/6 mice

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder which is caused by abrupt degeneration of dopaminergic neuronal cells in the substantia nigra pars compacta (SNPc) area of the midbrain. Adenosine A2A receptors have become promising therapeutic targets for PD; however, many A2A receptor antagonists face challenges, such as limited accessibility or failure in clinical trials due to poor selectivity and bioavailability. To identify novel A2A receptor antagonists, a 3D-QSAR-pharmacophore modeling approach was employed, involving virtual screening of ZINC, NCI, and MayBridge databases. The virtual hits were filtered via ADMET criteria to select compounds with favorable bioavailability and solubility profiles. From the MayBridge database, a potent monocyclic A2A receptor antagonist, AW00032 (N-(furan-2-ylmethyl)-5-methylthiazole-4-yl) thiophene-2-sulfonamide, was identified. AW00032 possessed key pharmacophoric features: two lipophilic hydrogen bond acceptors, one hydrophobic aliphatic/aromatic group, and one aromatic ring. Docking analysis revealed AW00032 had a strong binding affinity for A2A receptors (1.23 nM, ∆G - 10.49 kcal/mol), and its ADMET profile indicated good bioavailability. In 6-OHDA induced SH-SY5Y cells, AW00032 increased dopamine levels and tyrosine hydroxylase (TH) expression, demonstrating its potential as an A2A receptor antagonist. AW00032, discovered through 3D-QSAR pharmacophore modeling, also reduced reactive oxygen species (ROS) levels and showed depletion in mitochondrial dysfunction in 6-OHDA-induced SH-SY5Y cells. It exhibited A2A receptor antagonist activity comparable to the standard antagonist ZM241385, partially restoring dopamine and TH levels. Furthermore, AW00032 improved behavioral symptoms in haloperidol-induced C-57 bl/6 mice.

Linking machine learning and biophysical structural features in drug discovery

Introduction

Machine learning methods were applied to analyze pharmacophore features derived from four protein-binding sites, aiming to identify key features associated with ligand-specific protein conformations.

Methods

Using molecular dynamics simulations, we generated an ensemble of protein conformations to capture the dynamic nature of their binding sites. By leveraging pharmacophore descriptors, the AI/ML framework prioritized features uniquely associated with ligand-selected conformations, enabling a mechanism-driven understanding of binding interactions. This novel approach integrates biophysical insights with machine learning, focusing on pharmacophoric properties such as charge, hydrogen bonding, hydrophobicity, and aromaticity.

Results

Results showed significant enrichment of true positive ligands-improving database enrichment by up to 54-fold compared to random selection-demonstrating the robustness of this approach across diverse proteins.

Conclusion

Unlike conventional structure-based or ligand-based screening methods, this work emphasizes the role of specific protein conformations in driving ligand binding, making the process highly interpretable and actionable for drug discovery. The key innovation lies in identifying pharmacophore features tied to conformations selected by ligands, offering a predictive framework for optimizing drug candidates. This study illustrates the potential of combining ML and pharmacophoric analysis to develop intuitive and mechanism-driven tools for lead optimization and rational drug design.

CACHE Challenge #1: Docking with GNINA Is All You Need

We describe our winning submission to the first Critical Assessment of Computational Hit-Finding Experiments (CACHE) challenge. In this challenge, 23 participants employed a diverse array of structure-based methods to identify hits to a target with no known ligands. We utilized two methods, pharmacophore search and molecular docking, to identify our initial hit list and compounds for the hit expansion phase. Unlike many other participants, we limited ourselves to using docking scores in identifying and ranking hits. Our resulting best hit series tied for first place when evaluated by a panel of expert judges. Here, we report our top-performing open-source workflow and results.

Opiorphin: an endogenous human peptide with intriguing application in diverse range of pathologies

Mammalian zinc ectopeptidases have significant functions in deactivating neurological and hormonal peptide signals on the cell surface. The identification of Opiorphin, a physiological inhibitor of zinc ectopeptidases that inactivate enkephalin, has revealed its strong analgesic effects in both chemical and mechanical pain models. Opiorphin achieves this by increasing the transmission of endogenous opioids, which are dependent on the body's own opioid system. The function of opiorphin is closely linked to the rat sialorphin peptide, which inhibits pain perception by enhancing the activity of naturally occurring enkephalinergic pathways that depend on μ- and δ-opioid receptors. Opiorphin is highly intriguing in terms of its physiological implications within the endogenous opioidergic pathways, particularly in its ability to regulate mood-related states and pain perception. Opiorphin can induce antidepressant-like effects by influencing the levels of naturally occurring enkephalin, which are released in response to specific physical and/or psychological stimuli. This effect is achieved through the modulation of delta-opioid receptor-dependent pathways. Furthermore, research has demonstrated that opiorphin's impact on the cardiovascular system is facilitated by the renin-angiotensin system (RAS), sympathetic ganglia, and adrenal medulla, rather than the opioid system. Hence, opiorphin shows great potential as a solitary candidate for the treatment of several illnesses such as neurodegeneration, pain, and mood disorders.

A general strategy for the synthesis of taxane diterpenes

The carbon skeleton of any organic molecule serves as the foundation for its three-dimensional structure, playing a pivotal role in determining its physical and biological properties1. As such, taxane diterpenes are one of the most well-known natural product families, primarily owing to the success of their most prominent compound, paclitaxel, an effective anticancer therapeutic for more than 25 years2-6. In contrast to classical taxanes, the bioactivity of cyclotaxanes (also referred to as complex taxanes) remains significantly underexplored. The carbon skeletons of these two groups of taxanes differ significantly, and so would typically their own distinct synthetic approaches. Here we report a versatile synthetic strategy based on the interconversion of complex molecular frameworks, providing general access to the wider taxane diterpene family. A range of classical and cyclotaxane frameworks was prepared including, among others, the total syntheses of taxinine K (2), canataxapropellane (5) and dipropellane C from a single advanced intermediate. The synthetic approach deliberately eschews biomimicry, emphasizing instead the power of stereoelectronic control in orchestrating the interconversion of polycyclic frameworks.

bbSelect - An Open-Source Tool for Performing a 3D Pharmacophore-Driven Diverse Selection of R-Groups

The design of compounds during hit-to-lead often seeks to explore a vector from a core scaffold to form additional interactions with the target protein. A rational approach to this is to probe the region of a protein accessed by a vector with a systematic placement of pharmacophore features in 3D, particularly when bound structures are not available. Herein, we present bbSelect, an open-source tool built to map the placements of pharmacophore features in 3D Euclidean space from a library of R-groups, employing partitioning to drive a diverse and systematic selection to a user-defined size. An evaluation of bbSelect against established methods exemplified the superiority of bbSelect in its ability to perform diverse selections, achieving high levels of pharmacophore feature placement coverage with selection sizes of a fraction of the total set and without the introduction of excess complexity. bbSelect also reports visualizations and rationale to enable users to understand and interrogate results. This provides a tool for the drug discovery community to guide their hit-to-lead activities.

Discovery and Preclinical Characterization of BIIB129, a Covalent, Selective, and Brain-Penetrant BTK Inhibitor for the Treatment of Multiple Sclerosis

Multiple sclerosis (MS) is a chronic disease with an underlying pathology characterized by inflammation-driven neuronal loss, axonal injury, and demyelination. Bruton's tyrosine kinase (BTK), a nonreceptor tyrosine kinase and member of the TEC family of kinases, is involved in the regulation, migration, and functional activation of B cells and myeloid cells in the periphery and the central nervous system (CNS), cell types which are deemed central to the pathology contributing to disease progression in MS patients. Herein, we describe the discovery of BIIB129 (25), a structurally distinct and brain-penetrant targeted covalent inhibitor (TCI) of BTK with an unprecedented binding mode responsible for its high kinome selectivity. BIIB129 (25) demonstrated efficacy in disease-relevant preclinical in vivo models of B cell proliferation in the CNS, exhibits a favorable safety profile suitable for clinical development as an immunomodulating therapy for MS, and has a low projected total human daily dose.

3D-QSAR pharmacophore modeling, virtual screening, molecular docking, MD simulations, in vitro and in vivo studies to identify potential anti-hyperplasia drugs

Psoriasis is a common immune-mediated skin condition characterized by aberrant keratinocytes and cell proliferation. The purpose of this study was to explore the FDA-approved drugs by 3D-QSAR pharmacophore model and evaluate their efficiency by in-silico, in vitro, and in vivo psoriasis animal model. A 3D-QSAR pharmacophore model was developed by utilizing HypoGen algorithm using the structural features of 48 diaryl derivatives with diverse molecular patterns. The model was validated by a test set of 27 compounds, by cost analysis method, and Fischer's randomization test. The correlation coefficient of the best model (Hypo2) was 0.9601 for the training set while it was 0.805 for the test set. The selected model was taken as a 3D query for the virtual screening of over 3000 FDA-approved drugs. Compounds mapped with the pharmacophore model were further screened through molecular docking. The hits that showed the best docking results were screened through in silico skin toxicity approach. Top five hits were selected for the MD simulation studies. Based on MD simulations results, the best two hit molecules, that is, ebastine (Ebs) and mebeverine (Mbv) were selected for in vitro and in vivo antioxidant studies performed in mice. TNF-α and COX pro-inflammatory mediators, biochemical assays, histopathological analyses, and immunohistochemistry observations confirmed the anti-inflammatory response of the selected drugs. Based on these findings, it appeared that Ebs can effectively treat psoriasis-like skin lesions and down-regulate inflammatory responses which was consistent with docking predictions and could potentially be employed for further research on inflammation-related skin illnesses such as psoriasis.

Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges

By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.

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.

Molecular dynamics articulated multilevel virtual screening protocol to discover novel dual PPAR α/γ agonists for anti-diabetic and metabolic applications

PPARα and PPARγ are isoforms of the nuclear receptor superfamily which regulate glucose and lipid metabolism. Activation of PPARα and PPARγ receptors by exogenous ligands could transactivate the expression of PPARα and PPARγ-dependent genes, and thereby, metabolic pathways get triggered, which are helpful to ameliorate treatment for the type 2 diabetes mellitus, and related metabolic complications. Herein, by understanding the structural requirements for ligands to activate PPARα and PPARγ proteins, we developed a multilevel in silico-based virtual screening protocol to identify novel chemical scaffolds and further design and synthesize two distinct series of glitazone derivatives with advantages over the classical PPARα and PPARγ agonists. Moreover, the synthesized compounds were biologically evaluated for PPARα and PPARγ transactivation potency from nuclear extracts of 3T3-L1 cell. Furthermore, glucose uptake assay on L6 cells confirmed the potency of the synthesized compounds toward glucose regulation. Percentage lipid-lowering potency was also assessed through triglyceride estimate from 3T3-L1 cell extracts. Results suggested the ligand binding mode was in orthosteric fashion as similar to classical agonists. Thus molecular docking and molecular dynamics (MD) simulation experiments were executed to validate our hypothesis on mode of ligands binding and protein complex stability. Altogether, the present study developed a newer protocol for virtual screening and enables to design of novel glitazones for activation of PPARα and PPARγ-mediated pathways. Accordingly, present approach will offer benefit as a therapeutic strategy against type 2 diabetes mellitus and associated metabolic complications.

Conformer Generation for Structure-Based Drug Design: How Many and How Good?

Conformer generation, the assignment of realistic 3D coordinates to a small molecule, is fundamental to structure-based drug design. Conformational ensembles are required for rigid-body matching algorithms, such as shape-based or pharmacophore approaches, and even methods that treat the ligand flexibly, such as docking, are dependent on the quality of the provided conformations due to not sampling all degrees of freedom (e.g., only sampling torsions). Here, we empirically elucidate some general principles about the size, diversity, and quality of the conformational ensembles needed to get the best performance in common structure-based drug discovery tasks. In many cases, our findings may parallel "common knowledge" well-known to practitioners of the field. Nonetheless, we feel that it is valuable to quantify these conformational effects while reproducing and expanding upon previous studies. Specifically, we investigate the performance of a state-of-the-art generative deep learning approach versus a more classical geometry-based approach, the effect of energy minimization as a postprocessing step, the effect of ensemble size (maximum number of conformers), and construction (filtering by root-mean-square deviation for diversity) and how these choices influence the ability to recapitulate bioactive conformations and perform pharmacophore screening and molecular docking.

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.

Discovery of novel RARα agonists using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulation studies

Nuclear retinoic acid receptors (RARs) are ligand-dependent transcription factors involved in various biological processes, such as embryogenesis, cell proliferation, differentiation, reproduction, and apoptosis. These receptors are regulated by retinoids, i.e., retinoic acid (RA) and its analogs, as receptor agonists. RAR agonists are promising therapeutic agents for the treatment of serious dermatological disorders, including some malignant conditions. By inducing apoptosis, they are able to inhibit the proliferation of diverse cancer cell lines. Also, RAR agonists have recently been identified as therapeutic options for some neurodegenerative diseases. These features make retinoids very attractive molecules for medical purposes. Synthetic selective RAR agonists have several advantages over endogenous ones, but they suffer poor pharmacokinetic properties. These compounds are normally lipophilic acids with unfavorable drug-like features such as poor oral bioavailability. Recently, highly selective, potent, and less toxic RAR agonists with proper lipophilicity, thus, good oral bioavailability have been developed for some therapeutic applications. In the present study, ligand and structure-based virtual screening technique was exploited to introduce some novel RARα agonists. Pharmacokinetic assessment was also performed in silico to suggest those compounds which have optimized drug-like features. Finally, two compounds with the best in silico pharmacological features are proposed as lead molecules for future development of RARα agonists.

A Shapeshifting Roadmap for Polycyclic Skeletal Evolution

Polycyclic ring systems are ubiquitous three-dimensional (3D) structural motifs central to the function of many biologically active small molecules and organic materials. Indeed, subtle changes to the overall molecular shape and connectivity of atoms in a polycyclic framework (i.e., isomerism) can drastically alter its function and properties. Unfortunately, direct evaluation of these structure-function relationships typically requires the development of distinct synthetic strategies toward a specific isomer. Dynamic, "shapeshifting" carbon cages present a promising approach for sampling isomeric chemical space but are often difficult to control and are largely limited to thermodynamic mixtures of positional isomers about a single core scaffold. Here, we describe the development of a new shapeshifting C9-chemotype and a chemical blueprint for its evolution into structurally and energetically diverse isomeric ring systems. By leveraging the unique molecular topology of π-orbitals interacting through-space (homoconjugation), a common skeletal ancestor evolved into a complex network of valence isomers. This unusual system represents an exceedingly rare small molecule capable of undergoing controllable and continuous isomerization processes through the iterative use of just two chemical steps (light and organic base). Computational and photophysical studies of the isomer network provide fundamental insight into the reactivity, mechanism, and role of homoconjugative interactions. Importantly, these insights may inform the rational design and synthesis of new dynamic, shapeshifting systems. We anticipate this process could be a powerful tool for the synthesis of structurally diverse, isomeric polycycles central to many bioactive small molecules and functional organic materials.

Combining the Classification and Pharmacophore Approaches to Understand Homogeneous Olfactory Perceptions at Peripheral Level: Focus on Two Aroma Mixtures

The mechanisms involved in the homogeneous perception of odorant mixtures remain largely unknown. With the aim of enhancing knowledge about blending and masking mixture perceptions, we focused on structure-odor relationships by combining the classification and pharmacophore approaches. We built a dataset of about 5000 molecules and their related odors and reduced the multidimensional space defined by 1014 fingerprints representing the structures to a tridimensional 3D space using uniform manifold approximation and projection (UMAP). The self-organizing map (SOM) classification was then performed using the 3D coordinates in the UMAP space that defined specific clusters. We explored the allocating in these clusters of the components of two aroma mixtures: a blended mixture (red cordial (RC) mixture, 6 molecules) and a masking binary mixture (isoamyl acetate/whiskey-lactone [IA/WL]). Focusing on clusters containing the components of the mixtures, we looked at the odor notes carried by the molecules belonging to these clusters and also at their structural features by pharmacophore modeling (PHASE). The obtained pharmacophore models suggest that WL and IA could have a common binding site(s) at the peripheral level, but that would be excluded for the components of RC. In vitro experiments will soon be carried out to assess these hypotheses.

An in silico molecular modeling approach of halolactone derivatives as potential inhibitors for human immunodeficiency virus type-1 reverse transcriptase enzyme

Acquired Immune Deficiency Syndrome (AIDS) is an infectious disease caused by Human Immunodeficiency Virus (HIV) infection and its replication requires the Reverse Transcriptase (RT) enzyme. RT plays a key role in the HIV life cycle, making it one of the most important targets for designing new drugs. Thus, in order to increase therapeutic options against AIDS, halolactone derivatives (D-halolactone) that have been showed as potential non-nucleoside inhibitors of the RT enzyme were studied. In the present work, a series of D-halolactone were investigated by molecular modeling studies, combining Three-dimensional Quantitative Structure-Activity Relationship (3 D-QSAR), molecular docking and Molecular Dynamics (MD) techniques, to understand the molecular characteristics that promote biological activity. The internal and external validation parameters indicated that the 3 D-QSAR model has good predictive capacity and statistical significance. Contour maps provided useful information on the structural characteristics of compounds for anti-HIV-1 activity. The docking results showed that D-halolactone present good complementarity by the RT allosteric site. In MD simulations it was observed that the formation of enzyme-ligand complexes were favorable, and from the free energy decomposition it was found that Leu100, Val106, Tyr181, Try188, and Trp229 are key residues for stabilization in the enzymatic site. Thus, the results showed that the proposed models can be used to design promising HIV-1 RT inhibitors. Communicated by Ramaswamy H. Sarma.

Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores

Fragment spaces are an efficient way to model large chemical spaces using a handful of small fragments and a few connection rules. The development of Enamine's REAL Space has shown that large spaces of readily available compounds may be created this way. These are several orders of magnitude larger than previous libraries. So far, searching and navigating these spaces is mostly limited to topological approaches. A way to overcome this limitation is optimization via metaheuristics which can be combined with arbitrary scoring functions. Here we present Galileo, a novel Genetic Algorithm to sample fragment spaces. We showcase Galileo in combination with a novel pharmacophore mapping approach, called Phariety, enabling 3D searches in fragment spaces. We estimate the effectiveness of the approach with a small fragment space. Furthermore, we apply Galileo to two pharmacophore searches in the REAL Space, detecting hundreds of compounds fulfilling a HSP90 and a FXIa pharmacophore.

Discovery of allosteric SHP2 inhibitors through ensemble-based consensus molecular docking, endpoint and absolute binding free energy calculations

SHP2 (Src homology-2 domain-containing protein tyrosine phosphatase-2) is a cytoplasmic protein -tyrosine phosphatase encoded by the gene PTPN11. It plays a crucial role in regulating cell growth and differentiation. Specifically, SHP2 is an oncoprotein associated with developmental pathologies and several different cancer types, including gastric, leukemia and breast cancer and is of great therapeutic interest. Given these roles, current research efforts have focused on developing SHP2 inhibitors. Allosteric SHP2 inhibitors have been shown to be more selective and pharmacologically appealing compared to competitive catalytic inhibitors targeting SHP2. Nevertheless, there remains a need for novel allosteric inhibitor scaffolds targeting SHP2 to develop compounds with improved selectivity, cell permeability, and bioavailability. Towards this goal, this study applied various computational tools to screen over 6 million compounds against the allosteric site within SHP2. The top-ranked hits from our in-silico screening were validated using protein thermal shift and biolayer interferometry assays, revealing three potent compounds. Kinetic binding assays were employed to measure the binding affinities of the top-ranked compounds and demonstrated that they all bind to SHP2 with a nanomolar affinity. Hence the compounds and the computational workflow described herein provide an effective approach for identifying and designing a generation of improved allosteric inhibitors of SHP2.

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning

Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.

Assessing How Residual Errors of Scoring Functions Correlate to Ligand Structural Features

Scoring functions (SFs) are ubiquitous tools for early stage drug discovery. However, their accuracy currently remains quite moderate. Despite a number of successful target-specific SFs appearing recently, up until now, no ideas on how to systematically improve the general scope of SFs have been formulated. In this work, we hypothesized that the specific features of ligands, corresponding to interactions well appreciated by medicinal chemists (e.g., hydrogen bonds, hydrophobic and aromatic interactions), might be responsible, in part, for the remaining SF errors. The latter provides direction to efforts aimed at the rational and systematic improvement of SF accuracy. In this proof-of-concept work, we took a CASF-2016 coreset of 285 ligands as a basis for comparison and calculated the values of scores for a representative panel of SFs (including AutoDock 4.2, AutoDock Vina, X-Score, NNScore2.0, ΔVina RF20, and DSX). The residual error of linear correlation of each SF value, with the experimental values of affinity and activity, was then analyzed in terms of its correlation with the presence of the fragments responsible for certain medicinal chemistry defined interactions. We showed that, despite the fact that SFs generally perform reasonably, there is room for improvement in terms of better parameterization of interactions involving certain fragments in ligands. Thus, this approach opens a potential way for the systematic improvement of SFs without their significant complication. However, the straightforward application of the proposed approach is limited by the scarcity of reliable available data for ligand-receptor complexes, which is a common problem in the field.

Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-Optimisation

Pharmacophores are an established concept for the modelling of ligand–receptor interactions based on the abstract representations of stereoelectronic molecular features. They became widely popular as filters for the fast virtual screening of large compound libraries. A lot of effort has been put into the development of sophisticated algorithms and strategies to increase the computational efficiency of the screening process. However, hardly any focus has been put on the development of automated procedures that optimise pharmacophores towards higher discriminatory power, which still has to be done manually by a human expert. In the age of machine learning, the researcher has become the decision-maker at the top level, outsourcing analysis tasks and recurrent work to advanced algorithms and automation workflows. Here, we propose an algorithm for the automated selection of features driving pharmacophore model quality using SAR information extracted from validated QPhAR models. By integrating the developed method into an end-to-end workflow, we present a fully automated method that is able to derive best-quality pharmacophores from a given input dataset. Finally, we show how the QPhAR-generated models can be used to guide the researcher with insights regarding (un-)favourable interactions for compounds of interest.

Machine learning approaches and their applications in drug discovery and design

This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug-drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non-linear datasets, as well as big data of increasing depth and complexity. Various machine learning-based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand-based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.

The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice

In the era of emerging microbial and non-communicable diseases and re-emerging microbial infections, the medical fraternity and the public are plagued by under-preparedness. It is evident by the severity of the Coronavirus disease (COVID-19) pandemic that novel microbial diseases are a challenge and are challenging to control. This is mainly attributed to the lack of complete knowledge of the novel microbe’s biology and pathogenesis and the unavailability of therapeutic drugs and vaccines to treat and control the disease. Clinical research is the only answer utilizing which can handle most of these circumstances. In this review, we highlight the importance of computer-assisted drug designing (CADD) and the aspects of molecular docking, molecular superimposition, 3D-pharmacophore technology, ethics, and good clinical practice (GCP) for the development of therapeutic drugs, devices, and vaccines.

Perceiving SARS-CoV-2 Mpro and PLpro dual inhibitors from pool of recognized antiviral compounds of endophytic microbes: an in silico simulation study

Coronavirus disease 2019 (COVID-19) persists and shook the global population where the endgame to this pandemic is brought on by developing vaccines in record-breaking time. Nevertheless, these vaccines are far from perfect where their efficiency ranges from 65 to 90%; therefore, vaccines are not the one only solution to overcome this situation, and apart from administration of vaccines, the scientific community is at quest for finding alternative solutions to incumber SARS-CoV-2 infection. In this study, our research group is keen on identifying a bioactive molecule that is independent in its mode of action from existing vaccines which can potentially target the SARS-CoV-2 virus replicative efficacy. Papain-like protease (PLpro) and main protease (Mpro) are the most lucrative targets of COVIDs against which the drugs can be developed, as these proteases play a vital role in the replication and development of viral particles. Researchers have modelled a compound such as GRL0617 and X77 as an inhibitor of Mpro and PLpro, respectively, but use of these compounds has several limitations on hosts like toxicity and solubility. Under the current study by deploying rigorous computational assessments, pool of microbial secondary metabolites was screened and handpicked to search a structural or functional analogue of GRL0617 and X77, with an idea to identify a compound that can serve as dual inhibitor for both PLpro and Mpro. From the manually curated database of known antiviral compounds from fungal origin, we found cytonic acids A and B to potentially serve as dual inhibitor of PLpro and Mpro.

Repurposing drugs for treatment of SARS-CoV-2 infection: computational design insights into mechanisms of action

The COVID-19 pandemic has negatively affected human life globally. It has led to economic crises and health emergencies across the world, spreading rapidly among the human population and has caused many deaths. Currently, there are no treatments available for COVID-19 so there is an urgent need to develop therapeutic interventions that could be used against the novel coronavirus infection. In this research, we used computational drug design technologies to repurpose existing drugs as inhibitors of SARS-CoV-2 viral proteins. The Broad Institute's Drug Repurposing Hub consists of in-development/approved drugs and was computationally screened to identify potential hits which could inhibit protein targets encoded by the SARS-CoV-2 genome. By virtually screening the Broad collection, using rationally designed pharmacophore features, we identified molecules which may be repurposed against viral nucleocapsid and non-structural proteins. The pharmacophore features were generated after careful visualisation of the interactions between co-crystalised ligands and the protein binding site. The ChEMBL database was used to determine the compound's level of inhibition of SARS-CoV-2 and correlate the predicted viral protein target with whole virus in vitro data. The results from this study may help to accelerate drug development against COVID-19 and the hit compounds should be progressed through further in vitro and in vivo studies on SARS-CoV-2.

Application of Site-Identification by Ligand Competitive Saturation in Computer-Aided Drug Design

Site Identification by Ligand Competitive Saturation (SILCS) is a molecular simulation approach that uses diverse small solutes in aqueous solution to obtain functional group affinity patterns of a protein or other macromolecule. This involves employing a combined Grand Canonical Monte Carlo (GCMC)-molecular dynamics (MD) method to sample the full 3D space of the protein, including deep binding pockets and interior cavities from which functional group free energy maps (FragMaps) are obtained. The information content in the maps, which include contributions from protein flexibilty and both protein and functional group desolvation contributions, can be used in many aspects of the drug discovery process. These include identification of novel ligand binding pockets, including allosteric sites, pharmacophore modeling, prediction of relative protein-ligand binding affinities for database screening and lead optimization efforts, evaluation of protein-protein interactions as well as in the formulation of biologics-based drugs including monoclonal antibodies. The present article summarizes the various tools developed in the context of the SILCS methodology and their utility in computer-aided drug design (CADD) applications, showing how the SILCS toolset can improve the drug-development process on a number of fronts with respect to both accuracy and throughput representing a new avenue of CADD applications.

Computational Approach to Combat COVID-19 Infection: Emerging Tool for Accelerating Drug Research

BACKGROUND

Drug discovery and development process is an expensive, complex, time-consuming and risky. There are different techniques involved in the drug development process which include random screening, computational approach, molecular manipulation and serendipitous research. Among these methods, the computational approach is considered as an efficient strategy to accelerate and economize the drug discovery process.

OBJECTIVE

This approach is mainly applied in various phases of drug discovery process including target identification, target validation, lead identification and lead optimization. Due to increase in the availability of information regarding various biological targets of different disease states, computational approaches such as molecular docking, de novo design, molecular similarity calculation, virtual screening, pharmacophore-based modeling and pharmacophore mapping have been applied extensively.

METHODS

Various drug molecules can be designed by applying computational tools to explore the drug candidates for treatment of Coronavirus infection. The world health organization has announced the novel corona virus disease as COVID-19 and declared it as pandemic globally on 11 February 2020. So, it is thought of interest to scientific community to apply computational methods to design and optimize the pharmacological properties of various clinically available and FDA approved drugs such as remdesivir, ribavirin, favipiravir, oseltamivir, ritonavir, arbidol, chloroquine, hydroxychloroquine, carfilzomib, baraticinib, prulifloxacin, etc for effective treatment of COVID-19 infection.

RESULTS

Further, various survey reports suggest that the extensive studies are carried out by various research communities to find out the safety and efficacy profile of these drug candidates.

CONCLUSION

This review is focused on the study of various aspects of these drugs related to their target sites on virus, binding interactions, physicochemical properties etc.

Greedy 3-Point Search (G3PS)-A Novel Algorithm for Pharmacophore Alignment

Chemical features of small molecules can be abstracted to 3D pharmacophore models, which are easy to generate, interpret, and adapt by medicinal chemists. Three-dimensional pharmacophores can be used to efficiently match and align molecules according to their chemical feature pattern, which facilitates the virtual screening of even large compound databases. Existing alignment methods, used in computational drug discovery and bio-activity prediction, are often not suitable for finding matches between pharmacophores accurately as they purely aim to minimize RMSD or maximize volume overlap, when the actual goal is to match as many features as possible within the positional tolerances of the pharmacophore features. As a consequence, the obtained alignment results are often suboptimal in terms of the number of geometrically matched feature pairs, which increases the false-negative rate, thus negatively affecting the outcome of virtual screening experiments. We addressed this issue by introducing a new alignment algorithm, Greedy 3-Point Search (G3PS), which aims at finding optimal alignments by using a matching-feature-pair maximizing search strategy while at the same time being faster than competing methods.

IRA: A Shape Matching Approach for Recognition and Comparison of Generic Atomic Patterns

We propose a versatile, parameter-less approach for solving the shape matching problem, specifically in the context of atomic structures when atomic assignments are not known a priori. The algorithm Iteratively suggests Rotated atom-centered reference frames and Assignments (iterative rotations and assignments (IRA)). The frame for which a permutationally invariant set-set distance, namely, the Hausdorff distance, returns a minimal value is chosen as the solution of the matching problem. IRA is able to find rigid rotations, reflections, translations, and permutations between structures with different numbers of atoms, for any atomic arrangement and pattern, periodic or not. When distortions are present between the structures, optimal rotation and translation are found by further applying a standard singular value decomposition-based method. To compute the atomic assignments under the one-to-one assignment constraint, we develop our own algorithm, constrained shortest distance assignments (CShDA). The overall approach is extensively tested on several structures, including distorted structural fragments. The efficiency of the proposed algorithm is shown as a benchmark comparison against two other shape matching algorithms. We discuss the use of our approach for the identification and comparison of structures and structural fragments through two examples: a replica-exchange trajectory of a cyanine molecule, in which we show how our approach could aid the exploration of relevant collective coordinates for clustering the data, and a SiO₂ amorphous model, in which we compute distortion scores, and compare them with a classical strain-based potential. The source code and benchmark data are available at https://github.com/mammasmias/IterativeRotationsAssignments.

Discovery of novel ID2 antagonists from pharmacophore-based virtual screening as potential therapeutics for glioma

Glioma, especially the most aggressive type glioblastoma multiforme, is a malignant cancer of the central nervous system with a poor prognosis. Traditional treatments are mainly surgery combined with radiotherapy and chemotherapy, which is still far from satisfactory. Therefore, it is of great clinical significance to find new therapeutic agents. Serving as an inhibitor of differentiation, protein ID2 (inhibitor of DNA binding 2) plays an important role in neurogenesis, neovascularization and malignant development of gliomas. It has been shown that ID2 affects the malignant progression of gliomas through different mechanisms. In this study, a pharmacophore-based virtual screening was carried out and 16 hit compounds were purchased for pharmacological evaluations on their ID2 inhibitory activities. Based on the cytotoxicity of these small-molecule compounds, two compounds were shown to effectively inhibit the viability of glioma cells in the micromolar range. Among them, AK-778-XXMU was chosen for further study due to its better solubility in water. A SPR (Surface Plasma Resonance) assay proved the high affinity between AK-778-XXMU and ID2 protein with the K_D value as 129 nM. The plausible binding mode of ID2 was studied by molecular docking and it was found to match AGX51 very well in the same binding site. Subsequently, the cancer-suppressing potency of the compound was characterized both in vitro and in vivo. The data demonstrated that compound AK-778-XXMU is a potent ID2 antagonist which has the potential to be developed as a therapeutic agent against glioma.

Metal Complexes for Therapeutic Applications

Metal complexes have been widely used for applications in the chemical and physical sciences due to their unique electronic and stereochemical properties. For decades the use of metal complexes for medicinal applications has been postulated and demonstrated. The distinct characteristics of metal complexes, including their molecular geometries (that are not readily accessed by organic molecules), as well as their ligand exchange, redox, catalytic, and photophysical reactions, give these compounds the potential to interact and react with biomolecules in unique ways and by distinct mechanisms of action. Herein, the potential of metal complexes to act as components bioactive therapeutic compounds is discussed.

Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model

DGAT1 plays a crucial controlling role in triglyceride biosynthetic pathways, which makes it an attractive therapeutic target for obesity. Thus, development of DGAT1 inhibitors with novel chemical scaffolds is desired and important in the drug discovery. In this investigation, the multistep virtual screening methods, including machine learning methods and common feature pharmacophore model, were developed and used to identify novel DGAT1 inhibitors from BioDiversity database with 30,000 compounds. 531 compounds were predicted as DGAT1 inhibitors by combination of machine learning methods comprising of SVM, NB and RP models. Then, 12 agents were filtered from 531 compounds by using the common feature pharmacophore model. The 3D chemical structures of the 12 hits coordinated with surface charges and isosurface have been carefully analyzed by the established 3D-QSAR model. Finally, 8 compounds with desired properties were retained from the final hits and have been assigned to another research group to complete the follow-up compound synthesis and biologic evaluation.

Exploring protein hotspots by optimized fragment pharmacophores

Fragment-based drug design has introduced a bottom-up process for drug development, with improved sampling of chemical space and increased effectiveness in early drug discovery. Here, we combine the use of pharmacophores, the most general concept of representing drug-target interactions with the theory of protein hotspots, to develop a design protocol for fragment libraries. The SpotXplorer approach compiles small fragment libraries that maximize the coverage of experimentally confirmed binding pharmacophores at the most preferred hotspots. The efficiency of this approach is demonstrated with a pilot library of 96 fragment-sized compounds (SpotXplorer0) that is validated on popular target classes and emerging drug targets. Biochemical screening against a set of GPCRs and proteases retrieves compounds containing an average of 70% of known pharmacophores for these targets. More importantly, SpotXplorer0 screening identifies confirmed hits against recently established challenging targets such as the histone methyltransferase SETD2, the main protease (3CLPro) and the NSP3 macrodomain of SARS-CoV-2.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

Hierarchical Graph Representation of Pharmacophore Models

For the investigation of protein-ligand interaction patterns, the current accessibility of a wide variety of sampling methods allows quick access to large-scale data. The main example is the intensive use of molecular dynamics simulations applied to crystallographic structures which provide dynamic information on the binding interactions in protein-ligand complexes. Chemical feature interaction based pharmacophore models extracted from these simulations, were recently used with consensus scoring approaches to identify potentially active molecules. While this approach is rapid and can be fully automated for virtual screening, additional relevant information from such simulations is still opaque and so far the full potential has not been entirely exploited. To address these aspects, we developed the hierarchical graph representation of pharmacophore models (HGPM). This single graph representation enables an intuitive observation of numerous pharmacophore models from long MD trajectories and further emphasizes their relationship and feature hierarchy. The resulting interactive depiction provides an easy-to-apprehend tool for the selection of sets of pharmacophores as well as visual support for analysis of pharmacophore feature composition and virtual screening results. Furthermore, the representation can be adapted to include information involving interactions between the same protein and multiple different ligands. Herein, we describe the generation, visualization and use of HGPMs generated from MD simulations of two x-ray crystallographic derived structures of the human glucokinase protein in complex with allosteric activators. The results demonstrate that a large number of pharmacophores and their relationships can be visualized in an interactive, efficient manner, unique binding modes identified and a combination of models derived from long MD simulations can be strategically prioritized for VS campaigns.

A probable means to an end: exploring P131 pharmacophoric scaffold to identify potential inhibitors of Cryptosporidium parvum inosine monophosphate dehydrogenase

Compound P131 has been established to inhibit Cryptosporidium parvum's inosine monophosphate dehydrogenase (CpIMPDH). Its inhibitory activity supersedes that of paromomycin, which is extensively used in treating cryptosporidiosis. Through the per-residue energy decomposition approach, crucial moieties of P131 were identified and subsequently adopted to create a pharmacophore model for virtual screening in the ZINC database. This search generated eight ADMET-compliant hits that were examined thoroughly to fit into the active site of CpIMPDH via molecular docking. Three compounds ZINC46542062, ZINC58646829, and ZINC89780094, with favorable docking scores of - 8.3 kcal/mol, - 8.2 kcal/mol, and - 7.5 kcal/mol, were selected. The potential inhibitory mechanism of these compounds was probed using molecular dynamics simulation and Molecular Mechanics Generalized Poisson Boltzmann Surface Area (MM/PBSA) analyses. Results revealed that one of the hits (ZINC46542062) exhibited a lower binding free energy of - 39.52 kcal/mol than P131, which had - 34.6 kcal/mol. Conformational perturbation induced by the binding of the identified hits to CpIMPDH was similar to P131, suggesting a similarity in inhibitory mechanisms. Also, in silico investigation of the properties of the hit compounds implied superior physicochemical properties with regards to their synthetic accessibility, lipophilicity, and number of hydrogen bond donors and acceptors in comparison with P131. ZINC46542062 was identified as a promising hit compound with the highest binding affinity to the target protein and favorable physicochemical and pharmacokinetic properties relative to P131. The identified compounds can serve as a basis for conducting further experimental investigations toward the development of anticryptosporidials, which can overcome the challenges of existing therapeutic options. Graphical abstract P131 and the identified compounds docked in the NAD⁺ binding site of Cryptosporidium parvum IMPDH.

ABBV-744 as a potential inhibitor of SARS-CoV-2 main protease enzyme against COVID-19

A new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and become pandemic with thousands new deaths and infected cases globally. To address coronavirus disease (COVID-19), currently no effective drug or vaccine is available. This necessity motivated us to explore potential lead compounds by considering drug repurposing approach targeting main protease (Mpro) enzyme of SARS-CoV-2. This enzyme considered to be an attractive drug target as it contributes significantly in mediating viral replication and transcription. Herein, comprehensive computational investigations were performed to identify potential inhibitors of SARS-CoV-2 Mpro enzyme. The structure-based pharmacophore modeling was developed based on the co-crystallized structure of the enzyme with its biological active inhibitor. The generated hypotheses were applied for virtual screening based PhaseScore. Docking based virtual screening workflow was used to generate hit compounds using HTVS, SP and XP based Glide GScore. The pharmacological and physicochemical properties of the selected lead compounds were characterized using ADMET. Molecular dynamics simulations were performed to explore the binding affinities of the considered lead compounds. Binding energies revealed that compound ABBV-744 binds to the Mpro with strong affinity (ΔGbind -45.43 kcal/mol), and the complex is more stable in comparison with other protein-ligand complexes. Our study classified three best compounds which could be considered as promising inhibitors against main protease SARS-CoV-2 virus.

Potential Inhibitors of Fascin From A Database of Marine Natural Products: A Virtual Screening and Molecular Dynamics Study

Marine nature products are unique compounds that are produced by the marine environment including plants, animals, and microorganisms. The wide diversity of marine natural products have great potential and are versatile in terms of drug discovery. In this paper, we use state-of-the-art computational methods to discover inhibitors from marine natural products to block the function of Fascin, an overexpressed protein in various cancers. First, virtual screening (pharmacophore model and molecular docking) was carried out based on a marine natural products database (12015 molecules) and provided eighteen molecules that could potentially inhibit the function of Fascin. Next, molecular mechanics generalized Born surface area (MM/GBSA) calculations were conducted and indicated that four molecules have higher binding affinities than the inhibitor NP-G2-029, which was validated experimentally. ADMET analyses of pharmacokinetics demonstrated that one of the four molecules does not match the criterion. Finally, ligand Gaussian accelerated molecular dynamics (LiGaMD) simulations were carried out to validate the three inhibitors binding to Fascin stably. In addition, dynamic interactions between protein and ligands were analyzed systematically. Our study will accelerate the development of the cancer drugs targeting Fascin.

The discovery of novel small molecule allosteric activators of aldehyde dehydrogenase 2

Aldehyde dehydrogenase 2 (ALDH2) plays important role in ethanol metabolism, and also serves as an important shield from the damage occurring under oxidative stress. A special inactive variant was found carried by 35-45% of East Asians. The variant carriers have recently been found at the higher risk for the diseases related to the damage occurring under oxidative stress, such as cardiovascular and cerebrovascular diseases. As a result, ALDH2 activators may potentially serve as a new class of therapeutics. Herein, N-benzylanilines were found as novel allosteric activators of ALDH2 by computational virtual screening using ligand-based and structure-based screening parallel screening strategy. Then a structural optimization was performed and has led to the discovery of the compound C6. It has good activity in vitro and in vivo, which could reduce infarct size by ∼70% in ischemic stroke rat models. This study provided good lead compounds for the further development of ALDH2 activators.

Applications of the Pharmacophore Concept in Natural Product inspired Drug Design

Pharmacophore-based techniques are nowadays an important part of many computer-aided drug design workflows and have been successfully applied for tasks such as virtual screening, lead optimization and de novo design. Natural products, on the other hand, can serve as a valuable source for unconventional molecular scaffolds that stimulate ideas for novel lead compounds in a more diverse chemical space that does not follow the rules of traditional medicinal chemistry. The first part of this review provides a brief introduction to the pharmacophore concept, the methods for pharmacophore model generation, and their applications. The second, concluding part, presents examples for recent, pharmacophore method related research in the field of natural product chemistry. The selected examples show, that pharmacophore-based methods which get mainly applied on synthetic drug-like molecules work equally well in the realm of natural products and thus can serve as a valuable tool for researchers in the field of natural product inspired drug design.

Ligand-based pharmacophore modeling of TNF-α to design novel inhibitors using virtual screening and molecular dynamics

Tumor necrosis factor-α (TNF-α) is one of the promising targets for treating inflammatory (Crohn disease, psoriasis, psoriatic arthritis, rheumatoid arthritis) and various other diseases. Commercially available TNF-α inhibitors are associated with several risks and limitations. In the present study, we have identified small TNF-α inhibitors using in silico approaches, namely pharmacophore modeling, virtual screening, molecular docking, molecular dynamics simulation and free binding energy calculations. The study yielded better and potent hits that bind to TNF-α with significant affinity. The best pharmacophore model generated using LigandScout has an efficient hit rate and Area Under the operating Curve. High throughput virtual screening of SPECS database molecules against crystal structure of TNF-α protein, coupled with physicochemical filtration, PAINS test. Virtual hit compounds used for molecular docking enabled the identification of 20 compounds with better binding energies when compared with previously known TNF-α inhibitors. MD simulation analysis on 20 virtual identified hits showed that ligand binding with TNF-α protein is stable and protein-ligand conformation remains unchanged. Further, 16 compounds passed ADMET analysis suggesting these identified hit compounds are suitable for designing a future class of potent TNF-α inhibitors.Communicated by Ramaswamy H. Sarma.

Dynamic-shared Pharmacophore Approach as Tool to Design New Allosteric PRC2 Inhibitors, Targeting EED Binding Pocket

The Polycomb Repressive complex 2 (PRC2) maintains a repressive chromatin state and silences many genes, acting as methylase on histone tails. This enzyme was found overexpressed in many types of cancer. In this work, we have set up a Computer-Aided Drug Design approach based on the allosteric modulation of PRC2. In order to minimize the possible bias derived from using a single set of coordinates within the protein-ligand complex, a dynamic workflow was developed. In details, molecular dynamic was used as tool to identify the most significant ligand-protein interactions from several crystallized protein structures. The identified features were used for the creation of dynamic pharmacophore models and docking grid constraints for the design of new PRC2 allosteric modulators. Our protocol was retrospectively validated using a dataset of active and inactive compounds, and the results were compared to the classic approaches, through ROC curves and enrichment factor. Our approach suggested some important interaction features to be adopted for virtual screening performance improvement.