Virtual screening of chemical libraries for drug discovery

Active and machine learning-enhanced discovery of new FGFR3 inhibitor, Rhapontin, through virtual screening of receptor structures and anti-cancer activity assessment

Introduction: This study bridges traditional remedies and modern pharmacology by exploring the synergy between natural compounds and Ceritinib in treating Non-Small Cell Lung Cancer (NSCLC), aiming to enhance efficacy and reduce toxicities. Methods: Using a combined approach of computational analysis, machine learning, and experimental procedures, we identified and analyzed PD173074, Isoquercitrin, and Rhapontin as potential inhibitors of fibroblast growth factor receptor 3 (FGFR3). Machine learning algorithms guided the initial selection, followed by Quantitative Structure-Activity Relationship (QSAR) modeling and molecular dynamics simulations to evaluate the interaction dynamics and stability of Rhapontin. Physicochemical assessments further verified its drug-like properties and specificity. Results: Our experiments demonstrate that Rhapontin, when combined with Ceritinib, significantly suppresses tumor activity in NSCLC while sparing healthy cells. The molecular simulations and physicochemical evaluations confirm Rhapontin's stability and favorable interaction with FGFR3, highlighting its potential as an effective adjunct in NSCLC therapy. Discussion: The integration of natural compounds with established cancer therapies offers a promising avenue for enhancing treatment outcomes in NSCLC. By combining the ancient wisdom of natural remedies with the precision of modern science, this study contributes to evolving cancer treatment paradigms, potentially mitigating the side effects associated with current therapies.

Hit discovery of potential CDK8 inhibitors and analysis of amino acid mutations for cancer therapy through computer-aided drug discovery

Cyclin-dependent kinase 8 (CDK8) has emerged as a promising target for inhibiting cancer cell function, intensifying efforts towards the development of CDK8 inhibitors as potential cancer therapeutics. Mutations in CDK8, a protein kinase, are also implicated as a primary factor associated with tumor formation. In this study, we identified potential inhibitors through virtual screening for CDK8 and single amino acid mutations in CDK8, namely D173A (Aspartate 173 mutate to Alanine), D189N (Aspartate 189 mutate to Asparagine), T196A (Threonine 196 mutate to Alanine) and T196D (Threonine 196 mutate to Aspartate). Four databases (CHEMBEL, ZINC, MCULE, and MolPort) containing 65,209,131 molecules have been searched to identify new inhibitors for CDK8 and its single mutations. In the first step, structure-based pharmacophore modeling in the Pharmit server was used to select the compounds to know the inhibitors. Then molecules with better predicted drug-like molecule properties were selected. The final filter used to select more effective inhibitors among the previously selected molecules was molecular docking. Finally, 13 hits for CDK8, 11 hits for D173A, 11 hits for D189N, 15 hits for T196A, and 12 hits for T196D were considered potential inhibitors. A majority of the virtual screening hits exhibited satisfactorily predict pharmacokinetic characteristics and toxicity properties.

Illuminating the structural basis of human neurokinin 1 receptor (NK1R) antagonism through classical all-atoms molecular dynamics simulations

Advances in structural biology have bestowed insights into the pleiotropic effects of neurokinin 1 receptors (NK1R) in diverse patho-physiological processes, thereby highlighting the potential therapeutic value of antagonists directed against NK1R. Herein, we investigate the mode of antagonist recognition to discern the obscure atomic facets germane for the function and molecular determinants of NK1R. To commence discernment of potent antagonists and the conformational changes in NK1R, induced upon antagonist binding, state-of-the-art classical all-atoms molecular dynamics (MD) simulations in lipid mimetic bilayers have been utilized. MD simulations of structural ensembles reveals the involvement of TM5 and TM6 in tight anchoring of antagonists through a network of interhelical hydrogen-bonds, while, the extracellular loop 2 (ECL2) governs the overall size and nature of the pocket, thereby modulating NK1R. Consistent comparison between experiments and MD simulation results discerns the predominant role of TM3, TM4, and TM6 in lipid-NK1R interaction. Correlation between hydrophobic index and helicity of TM domains elucidates their importance in maintaining the structural stability in addition to regulating NK1R antagonism. Taken together, we anticipate that our computational study marks a comprehensive structural basis of NK1R antagonism in lipid bilayers, which may facilitate designing of new therapeutics against associated diseases targeting human neurokinin receptors.

Recombinant protein production for structural and kinetic studies: A case study using M. tuberculosis α-methylacyl-CoA racemase (MCR)

Modern drug discovery is a target-driven approach in which a particular protein such as an enzyme is implicated in the disease process. Commonly, small-molecule drugs are identified using screening, rational design, and structural biology approaches. Drug screening, testing and optimization is typically conducted in vitro, and copious amounts of protein are required. The advent of recombinant DNA technologies has resulted in a rise in proteins purified by affinity techniques, typically by incorporating an "affinity tag" at the N- or C-terminus. Use of these tagged proteins and affinity techniques comes with a host of issues. This chapter describes the production of an untagged enzyme, α-methylacyl-CoA racemase (MCR) from Mycobacterium tuberculosis, using a recombinant E. coli system. Purification of the enzyme on a 100 mg scale using tandem anion-exchange chromatographies (DEAE-sepharose and RESOURCE-Q columns), and size-exclusion chromatographies is described. A modified protocol allowing the purification of cationic proteins is also described, based on tandem cation-exchange chromatographies (using CM-sepharose and RESOURCE-S columns) and size-exclusion chromatographies. The resulting MCR protein is suitable for biochemical and structural biology applications. The described protocols have wide applicability to the purification of other recombinant proteins and enzymes without using affinity chromatography.

Steady-state kinetic analysis of reversible enzyme inhibitors: A case study on calf intestine alkaline phosphatase

Enzymes are important drug targets and inhibition of enzymatic activity is an important therapeutic strategy. Enzyme assays measuring catalytic activity are utilized in both the discovery and development of new drugs. Colorimetric assays based on the release of 4-nitrophenol from substrates are commonly used. 4-Nitrophenol is only partly ionized to 4-nitrophenolate under typical assay conditions (pH 7-9) leading to under-estimation of product formation rates due to the much lower extinction coefficient of 4-nitrophenol compared to 4-nitrophenolate. Determination of 4-nitrophenol pKa values based on absorbance at 405 nm as a function of experimental pH values is reported, allowing for calculation of a corrected extinction coefficient at the assay pH. Characterization of inhibitor properties using steady-state enzyme kinetics is demonstrated using calf intestine alkaline phosphatase and 4-nitrophenyl phosphate as substrate at pH ∼8.2. The following kinetic parameters were determined: Km= 40±3 µM; Vmax= 72.8±1.2 µmolmin-1mg protein-1; kcat= 9.70±0.16 s-1; kcat/Km= 2.44±0.16 × 105 M-1s-1 (mean± SEM, N = 4). Sodium orthovanadate and EDTA were used as model inhibitors and the following pIC50 values were measured using dose-response curves: 6.61±0.08 and 3.07±0.03 (mean±SEM, N = 4). Rapid dilution experiments determined that inhibition was reversible for sodium orthovanadate and irreversible for EDTA. A Ki value for orthovanadate of 51±8 nM (mean±SEM, N = 3) was determined. Finally, data analysis and statistical design of experiments are discussed.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Natural Inhibitors Targeting the Localization of Lipoprotein System in Vibrio parahaemolyticus

The localization of lipoprotein (Lol) system is responsible for the transport of lipoproteins in the outer membrane (OM) of Vibrio parahaemolyticus. LolB catalyzes the last step in the Lol system, where lipoproteins are inserted into the OM. If the function of LolB is impeded, growth of V. parahaemolyticus is inhibited, due to lack of an intact OM barrier for protection against the external environment. Additionally, it becomes progressively harder to generate antimicrobial resistance (AMR). In this study, LolB was employed as the receptor for a high-throughput virtual screening from a natural compounds database. Compounds with higher glide score were selected for an inhibition assay against V. parahaemolyticus. It was found that procyanidin, stevioside, troxerutin and rutin had both exciting binding affinity with LolB in the micromolar range and preferable antibacterial activity in a concentration-dependent manner. The inhibition rates of 100 ppm were 87.89%, 86.2%, 91.39% and 83.71%, respectively. The bacteriostatic mechanisms of the four active compounds were explored further via fluorescence spectroscopy and molecular docking, illustrating that each molecule formed a stable complex with LolB via hydrogen bonds and pi-pi stacking interactions. Additionally, the critical sites for interaction with V. parahaemolyticus LolB, Tyr108 and Gln68, were also illustrated. This paper demonstrates the inhibition of LolB, thus, leading to antibacterial activity, and identifies LolB as a promising drug target for the first time. These compounds could be the basis for potential antibacterial agents against V. parahaemolyticus.

Why 90% of clinical drug development fails and how to improve it?

Ninety percent of clinical drug development fails despite implementation of many successful strategies, which raised the question whether certain aspects in target validation and drug optimization are overlooked? Current drug optimization overly emphasizes potency/specificity using structure‒activity-relationship (SAR) but overlooks tissue exposure/selectivity in disease/normal tissues using structure‒tissue exposure/selectivity-relationship (STR), which may mislead the drug candidate selection and impact the balance of clinical dose/efficacy/toxicity. We propose structure‒tissue exposure/selectivity-activity relationship (STAR) to improve drug optimization, which classifies drug candidates based on drug's potency/selectivity, tissue exposure/selectivity, and required dose for balancing clinical efficacy/toxicity. Class I drugs have high specificity/potency and high tissue exposure/selectivity, which needs low dose to achieve superior clinical efficacy/safety with high success rate. Class II drugs have high specificity/potency and low tissue exposure/selectivity, which requires high dose to achieve clinical efficacy with high toxicity and needs to be cautiously evaluated. Class III drugs have relatively low (adequate) specificity/potency but high tissue exposure/selectivity, which requires low dose to achieve clinical efficacy with manageable toxicity but are often overlooked. Class IV drugs have low specificity/potency and low tissue exposure/selectivity, which achieves inadequate efficacy/safety, and should be terminated early. STAR may improve drug optimization and clinical studies for the success of clinical drug development.

Identifying SARS-CoV-2 main protease inhibitors by applying the computer screening of a large database of molecules

The outbreak of coronavirus disease 2019 (COVID-19) at the end of 2019 affected global health. Its infection agent was called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Wearing a mask, maintaining social distance, and vaccination are effective ways to prevent infection of SARS-CoV-2, but none of them help infected people. Targeting the enzymes of SARS-CoV-2 is an effective way to stop the replication of the virus in infected people and treat COVID-19 patients. SARS-CoV-2 main protease is a therapeutic target which the inhibition of its enzymatic activity prevents from the replication of SARS-CoV-2. A large database of molecules has been searched to identify new inhibitors for SARS-CoV-2 main protease enzyme. At the first step, ligand screening based on similarity search was used to select similar compounds to known SARS-CoV-2 main protease inhibitors. Then molecules with better predicted pharmacokinetic properties were selected. Structure-based virtual screening based on the application of molecular docking and molecular dynamics simulation methods was used to select more effective inhibitors among selected molecules in previous step. Finally two compounds were considered as SARS-CoV-2 main protease inhibitors.

Multiple machine learning models combined with virtual screening and molecular docking to identify selective human ALDH1A1 inhibitors

Aldehyde dehydrogenases (ALDHs) are the enzymes of oxidoreductase family that are responsible for the aldehyde metabolism. The unbalanced expression of these enzymes may be associated with a variety of disease conditions including cancers. ALDH1A1 is one of the isoform of ALDHs majorly overexpressed in a variety of tumors and responsible for the anti-cancer drug resistance. This makes ALDH1A1 as a specific target to develop small molecule ALDH1A1 inhibitors for resistant cancer condition. Number of ALDH1A1 inhibitors have been developed and reported in the literature, but because of non-selectivity and inappropriate pharmacokinetic properties till now none of these have reached in the market for clinical use. Therefore, multiple machine learning models of different isoforms of ALDHs are integrated with in-silico techniques including virtual screening, docking, ADMET profiling, and MD simulation to identify selective ALDH1A1 inhibitors. Total ten selective ALDH1A1 inhibitors with diverse scaffolds and appropriate ADMET were identified that can be further developed as adjuvant therapy in cyclophosphamide and cisplatin resistance cancer.

Multiple machine learning, molecular docking, and ADMET screening approach for identification of selective inhibitors of CYP1B1

Cytochrome P4501B1 is a ubiquitous family protein that is majorly overexpressed in tumors and is responsible for biotransformation-based inactivation of anti-cancer drugs. This inactivation marks the cause of resistance to chemotherapeutics. In the present study, integrated in-silico approaches were utilized to identify selective CYP1B1 inhibitors. To achieve this objective, we initially developed different machine learning models corresponding to two isoforms of the CYP1 family i.e. CYP1A1 and CYP1B1. Subsequently, small molecule databases including ChemBridge, Maybridge, and natural compound library were screened from the selected models of CYP1B1 and CYP1A1. The obtained CYP1B1 inhibitors were further subjected to molecular docking and ADMET analysis. The selectivity of the obtained hits for CYP1B1 over the other isoforms was also judged with molecular docking analysis. Finally, two hits were found to be the most stable which retained key interactions within the active site of CYP1B1 after the molecular dynamics simulations. Novel compound with CYP-D9 and CYP-14 IDs were found to be the most selective CYP1B1 inhibitors which may address the issue of resistance. Moreover, these compounds can be considered as safe agents for further cell-based and animal model studies.Communicated by Ramaswamy H. Sarma.

The strategies and techniques of drug discovery from natural products

Natural products have been the main sources of new drugs. The different strategies have been developed to find the new drugs based on natural products. The traditional and ethic medicines have provided information on the therapeutic effects and resulted in some notable drug discovery of natural products. The special activities of the medicine plants such as the side effects have inspired scientists to develop the novel small molecular. The microorganisms and the endogenous active substances from human or animal also become the important approaches to the drug discovery. The tremendous progress in technology led to the new strategies in drug discovery from natural products. The bioinformation and artificial intelligence have facilitated the research and development of natural products. We will provide a scene of strategies and technologies for drug discovery from natural products in this review.

RNA-dependent RNA polymerase of SARS-CoV-2 as a therapeutic target

The global pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), named coronavirus disease 2019, has infected more than 8.9 million people worldwide. This calls for urgent effective therapeutic measures. RNA-dependent RNA polymerase (RdRp) activity in viral transcription and replication has been recognized as an attractive target to design novel antiviral strategies. Although SARS-CoV-2 shares less genetic similarity with SARS-CoV (~79%) and Middle East respiratory syndrome coronavirus (~50%), the respective RdRps of the three coronaviruses are highly conserved, suggesting that RdRp is a good broad-spectrum antiviral target for coronaviruses. In this review, we discuss the antiviral potential of RdRp inhibitors (mainly nucleoside analogs) with an aim to provide a comprehensive account of drug discovery on SARS-CoV-2.

Integration of Lead Discovery Tactics and the Evolution of the Lead Discovery Toolbox

There has been much debate around the success rates of various screening strategies to identify starting points for drug discovery. Although high-throughput target-based and phenotypic screening has been the focus of this debate, techniques such as fragment screening, virtual screening, and DNA-encoded library screening are also increasingly reported as a source of new chemical equity. Here, we provide examples in which integration of more than one screening approach has improved the campaign outcome and discuss how strengths and weaknesses of various methods can be used to build a complementary toolbox of approaches, giving researchers the greatest probability of successfully identifying leads. Among others, we highlight case studies for receptor-interacting serine/threonine-protein kinase 1 and the bromo- and extra-terminal domain family of bromodomains. In each example, the unique insight or chemistries individual approaches provided are described, emphasizing the synergy of information obtained from the various tactics employed and the particular question each tactic was employed to answer. We conclude with a short prospective discussing how screening strategies are evolving, what this screening toolbox might look like in the future, how to maximize success through integration of multiple tactics, and scenarios that drive selection of one combination of tactics over another.

Predicting the Conformational Variability of Abl Tyrosine Kinase using Molecular Dynamics Simulations and Markov State Models

Understanding protein conformational variability remains a challenge in drug discovery. The issue arises in protein kinases, whose multiple conformational states can affect the binding of small-molecule inhibitors. To overcome this challenge, we propose a comprehensive computational framework based on Markov state models (MSMs). Our framework integrates the information from explicit-solvent molecular dynamics simulations to accurately rank-order the accessible conformational variants of a target protein. We tested the methodology using Abl kinase with a reference and blind-test set. Only half of the Abl conformational variants discovered by our approach are present in the disclosed X-ray structures. The approach successfully identified a protein conformational state not previously observed in public structures but evident in a retrospective analysis of Lilly in-house structures: the X-ray structure of Abl with WHI-P154. Using a MSM-derived model, the free energy landscape and kinetic profile of Abl was analyzed in detail highlighting opportunities for targeting the unique metastable states.

A novel identification approach for discovery of 5-HydroxyTriptamine 2A antagonists: combination of 2D/3D similarity screening, molecular docking and molecular dynamics

5-HydroxyTriptamine 2A antagonists are potential targets for treatment of various cerebrovascular and cardiovascular disorders. In this study, we have developed and performed a unique screening pipeline for filtering ZINC database compounds on the basis of similarities to known antagonists to determine novel small molecule antagonists of 5-HydroxyTriptamine 2A. The screening pipeline is based on 2D similarity, 3D dissimilarity and a combination of 2D/3D similarity. The shortlisted compounds were docked to a 5-HydroxyTriptamine 2A homology-based model, and complexes with low binding energies (287 complexes) were selected for molecular dynamics (MD) simulations in a lipid bilayer. The MD simulations of the shortlisted compounds in complex with 5-HydroxyTriptamine 2A confirmed the stability of the complexes and revealed novel interaction insights. The receptor residues S239, N343, S242, S159, Y370 and D155 predominantly participate in hydrogen bonding. π-π stacking is observed in F339, F340, F234, W151 and W336, whereas hydrophobic interactions are observed amongst V156, F339, F234, V362, V366, F340, V235, I152 and W151. The known and potential antagonists shortlisted by us have similar overlapping molecular interaction patterns. The 287 potential 5-HydroxyTriptamine 2A antagonists may be experimentally verified.

The future of virtual compound screening

We provide a future perspective of the virtual screening field. A number of challenges will be highlighted that virtual screening will likely face when compound data will further grow at or beyond current rates and when much more target information will become available. These challenges go beyond computational efficiency issues (that will of course also play a critical role). For example, for structure-based approaches, the accuracy of scoring functions and energy calculations will need to be improved. For ligand-based approaches, the compound class-dependence of similarity methods needs to be further explored and relationships between molecular similarity and activity similarity need to be established. We also comment on the current and future value of virtual screening. Opportunities for further development in a postgenome era are also discussed. It is hoped that some of the views and hypotheses we articulate might stimulate further discussion about the virtual screening field going forward.

Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity

Profile-QSAR is a novel 2D predictive model building method for kinases. This "meta-QSAR" method models the activity of each compound against a new kinase target as a linear combination of its predicted activities against a large panel of 92 previously studied kinases comprised from 115 assays. Profile-QSAR starts with a sparse incomplete kinase by compound (KxC) activity matrix, used to generate Bayesian QSAR models for the 92 "basis-set" kinases. These Bayesian QSARs generate a complete "synthetic" KxC activity matrix of predictions. These synthetic activities are used as "chemical descriptors" to train partial-least squares (PLS) models, from modest amounts of medium-throughput screening data, for predicting activity against new kinases. The Profile-QSAR predictions for the 92 kinases (115 assays) gave a median external R²(ext) = 0.59 on 25% held-out test sets. The method has proven accurate enough to predict pairwise kinase selectivities with a median correlation of R²(ext) = 0.61 for 958 kinase pairs with at least 600 common compounds. It has been further expanded by adding a "C(k)XC" cellular activity matrix to the KxC matrix to predict cellular activity for 42 kinase driven cellular assays with median R²(ext) = 0.58 for 24 target modulation assays and R²(ext) = 0.41 for 18 cell proliferation assays. The 2D Profile-QSAR, along with the 3D Surrogate AutoShim, are the foundations of an internally developed iterative medium-throughput screening (IMTS) methodology for virtual screening (VS) of compound archives as an alternative to experimental high-throughput screening (HTS). The method has been applied to 20 actual prospective kinase projects. Biological results have so far been obtained in eight of them. Q² values ranged from 0.3 to 0.7. Hit-rates at 10 uM for experimentally tested compounds varied from 25% to 80%, except in K5, which was a special case aimed specifically at finding "type II" binders, where none of the compounds were predicted to be active at 10 μM. These overall results are particularly striking as chemical novelty was an important criterion in selecting compounds for testing. The method is completely automated. Predicted activities for nearly 4 million internal and commercial compounds across 115 kinase assays and 42 cellular assays are stored in the corporate database. Like computed physical properties, this predicted kinase activity profile can be computed and stored as each compound is registered.

Pharmacophore-based virtual screening

This chapter is a review of the most recent developments in the field of pharmacophore modeling, covering both methodology and application. Pharmacophore-based virtual screening is nowadays a mature technology, very well accepted in the medicinal chemistry laboratory. Nevertheless, like any empirical approach, it has specific limitations and efforts to improve the methodology are still ongoing. Fundamentally, the core idea of "stripping" functional groups of their actual chemical nature in order to classify them into very few pharmacophore types, according to their dominant physico-chemical features, is both the main advantage and the main drawback of pharmacophore modeling. The advantage is the one of simplicity - the complex nature of noncovalent ligand binding interactions is rendered intuitive and comprehensible by the human mind. Although computers are much better suited for comparisons of pharmacophore patterns, a chemist's intuition is primarily scaffold-oriented. Its underlying simplifications render pharmacophore modeling unable to provide perfect predictions of ligand binding propensities - not even if all its subsisting technical problems would be solved. Each step in pharmacophore modeling and exploitation has specific drawbacks: from insufficient or inaccurate conformational sampling to ambiguities in pharmacophore typing (mainly due to uncertainty regarding the tautomeric/protonation status of compounds), to computer time limitations in complex molecular overlay calculations, and to the choice of inappropriate anchoring points in active sites when ligand cocrystals structures are not available. Yet, imperfections notwithstanding, the approach is accurate enough in order to be practically useful and actually is the most used virtual screening technique in medicinal chemistry - notably for "scaffold hopping" approaches, allowing the discovery of new chemical classes carriers of a desired biological activity.

Correlation between performance of QM/MM docking and simple classification of binding sites

Use of SiteMap for binding site classification and its connection to QM/MM (quantum mechanics/ molecular mechanics) docking performance were investigated. Using the hydrophilic/hydrophobic character values along with balance between them which SiteMap calculates, we sorted 455 cocrystal complexes available from protein data bank into three groups and tested how Glide, a conventional docking program, and QPLD, a QM/MM docking program, perform on them. QPLD showed improvements on all three groups over Glide but with varying degrees. Analysis of the results was carried out, and establishment of correlations between the classification of binding sites and QM/MM docking performance was attempted. It was found that QM/MM docking delivers the most improvements for primarily hydrophobic binding sites with substantial hydrophilic interactions. Based on our findings, we make suggestions for use of QM/MM docking and directions for further developments.