High Speed Development and Synthesis of Novel Small Molecule Libraries

Combinatorial chemistry has produced libraries of millions of compounds in the last decade. Screening of those compounds, unfortunately, has not yet yielded as many new drug candidates as initially expected. Among a number of possible reasons, one is that many libraries combinatorial chemistry produced in the early periods are of the nature of linear, flat, and flexible molecules such as peptides and oligonucleotides, which do not have the desired properties to selectively interact with their targets to yield high quality hits and leads. In order to increase the number of quality hits and leads, rigid, structural featurerich and drug-like compound libraries are highly desirable. Design and development of structural features-rich and natural product-like combinatorial libraries, as well as high speed library production using modern solution and solid phase synthesis techniques such as IRORI's Directed Sorting technology, will be discussed.     

Antiviral Activity of Metabolites from Peruvian Plants against SARS-CoV-2: An In Silico Approach

(1) Background: The COVID-19 pandemic lacks treatments; for this reason, the search for potential compounds against therapeutic targets is still necessary. Bioinformatics tools have allowed the rapid in silico screening of possible new metabolite candidates from natural resources or repurposing known ones. Thus, in this work, we aimed to select phytochemical candidates from Peruvian plants with antiviral potential against three therapeutical targets of SARS-CoV-2. (2) Methods: We applied in silico technics, such as virtual screening, molecular docking, molecular dynamics simulation, and MM/GBSA estimation. (3) Results: Rutin, a compound present in Peruvian native plants, showed affinity against three targets of SARS-CoV-2. The molecular dynamics simulation demonstrated the high stability of receptor–ligand systems during the time of the simulation. Our results showed that the Mpro-Rutin system exhibited higher binding free energy than PLpro-Rutin and N-Rutin systems through MM/GBSA analysis. (4) Conclusions: Our study provides insight on natural metabolites from Peruvian plants with therapeutical potential. We found Rutin as a potential candidate with multiple pharmacological properties against SARS-CoV-2.     

Structure based pharmacophore study to identify possible natural selective PARP-1 trapper as anti-cancer agent

Inhibition of poly(ADP-ribose) polymerase-1 (PARP-1) has turned out an innovative approach for cancer therapy due to its involvement in DNA repair pathways. Although several potent PARP-1 inhibitors have been identified, they exhibit high toxicity, resistivity and diverse pharmacological profile in clinical trials, which necessitate for extensive investigation and development of selective inhibitors. Therefore, the study aimed to identify selective natural PARP-1 inhibitors to reduce toxicity and resistivity with high potency. Accordingly, the combined approach of structure-based pharmacophore and molecular docking study was performed. Hence, the two hits (SN00167272 and STOCK1N-92279) were identified to have all the pharmacophoric features that showed interaction with key residues (Gly863, Ser904, Tyr896, and Tyr907) and least conserved residues (Tyr889 and Asp766). Additionally, these inhibitors represented interactions with unique selective residues (Asp756, Val762, Glu763 and Val886) and exhibited strong interaction with PARP-1 through binding free energy and molecular dynamics study. Hence, the identified hits could further considered for experimental investigations as they may reduce off-target and resistivity of currently available inhibitors and developed as potential anti-cancer agents in the future. Also, the study provides a specific structural insight which could further help to design selective and potent PARP-1 inhibitors.     

Identification of Potential SARS-CoV-2 Main Protease and Spike Protein Inhibitors from the Genus Aloe: An In Silico Study for Drug Development

Severe acute respiratory syndrome coronavirus (SARS-CoV-2) disease is a global rapidly spreading virus showing very high rates of complications and mortality. Till now, there is no effective specific treatment for the disease. Aloe is a rich source of isolated phytoconstituents that have an enormous range of biological activities. Since there are no available experimental techniques to examine these compounds for antiviral activity against SARS-CoV-2, we employed an in silico approach involving molecular docking, dynamics simulation, and binding free energy calculation using SARS-CoV-2 essential proteins as main protease and spike protein to identify lead compounds from Aloe that may help in novel drug discovery. Results retrieved from docking and molecular dynamics simulation suggested a number of promising inhibitors from Aloe. Root mean square deviation (RMSD) and root mean square fluctuation (RMSF) calculations indicated that compounds 132, 134, and 159 were the best scoring compounds against main protease, while compounds 115, 120, and 131 were the best scoring ones against spike glycoprotein. Compounds 120 and 131 were able to achieve significant stability and binding free energies during molecular dynamics simulation. In addition, the highest scoring compounds were investigated for their pharmacokinetic properties and drug-likeness. The Aloe compounds are promising active phytoconstituents for drug development for SARS-CoV-2.     

Molecular modeling studies to discover novel mIDH2 inhibitors with high selectivity for the primary and secondary mutants

Mutant isocitrate dehydrogenase 2 (mIDH2) is an emerging target for the treatment of cancer. AG-221 is the first mIDH2 inhibitor approved by the FDA for acute myeloid leukemia treatment, but its acquired resistance has recently been observed, necessitating the development of new inhibitor. In this study, a multi-step virtual screening protocol was employed for the analysis of a large database of compounds to identify potential mIDH2 inhibitors. To this end, we firstly utilized molecular dynamics (MD) simulations and binding free energy calculations to elucidate the key factors affecting ligand binding and drug resistance. Based on these findings, the receptor-ligand interaction-based pharmacophore (IBP) model and hierarchical docking-based virtual screening were sequentially carried out to assess 212,736 compounds from the Specs database. The resulting hits were finally ranked by PAINS filter and ADME prediction and the top compounds were obtained. Among them, six molecules were identified as mIDH2 putative inhibitors with high selectivity by interacting with the capping residue Asp312. Furthermore, subsequent docking and MD experiments demonstrated that compound V2 might have potential inhibitory activity against the AG-221-resistant mutants, thereby making it a promising lead for the development of novel mIDH2 inhibitors.     

Computational platform Way2Drug: from the prediction of biological activity to drug repurposing

The Way2Drug informational-computational platform (www.way2drug.com/dr) provides access to the data on drugs approved for medicinal use in the USA and Russian Federation, as well as computational possibilities for the prediction of biological activity of drug-like organic compounds. Currently realized computational tools of the platform, which allow one to predict several thousands of biological activity types, including the interaction with molecular targets, pharmacotherapeutic and side effects, metabolism, acute toxicity for rats, cytotoxicity, influence on gene expression, and other properties characterizing the evaluation how promising are particular drug-like compounds as potential pharmaceuticals, are reviewed. Using the Way2Drug platform, one can not only select the most promising "hits" for the synthesis and testing of biological activity but also reveal new indications for the launched drugs.     

Identification of natural compounds targeting SARS-CoV-2 Mpro by virtual screening and molecular dynamics simulations

The SARS-CoV-2 main protease (Mpro) has been chosen as a conserved molecular target to develop broad-spectrum antiviral drugs. Using molecular docking and molecular dynamics (MD) simulations, a total of 5600 natural compounds available for virtual screening were tested to identify potential inhibitors of SARS-CoV-2 Mpro. As a result, three natural compounds (pentagalloylglucose, malonylawobanin and gnetin E dihydride) were found to be potential inhibitors of SARS-CoV-2, which confirms the theoretical and practical significance of this approach for the design of SARS-CoV-2 inhibitors.     

Gp41 inhibitory activity prediction of theaflavin derivatives using ligand/structure-based virtual screening approaches

Gp41 and its conserved hydrophobic groove on the NHR region is one of the attractive targets in the design of HIV-1 entry inhibitory agents. This hydrophobic pocket is very critical for the progression of HIV and host cell fusion. In this study different ligand-based (structure similarity search) and structure-based (molecular docking and molecular dynamic simulation) methods were performed in a virtual screening procedure to select the best compounds with the most probable HIV-1 gp41 inhibitory activities. In silico pharmacokinetics and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties filtration also was considered to choose the compounds with best drug-like properties. The results of molecular docking and molecular dynamic simulations of the final selected compounds showed suitable stabilities of their complexes with gp41. The final selected hits could have better pharmacokinetics properties than the template compound, theaflavin digallate (TF₃), a naturally-originated potent gp41 inhibitor.     

Collection and preparation of molecular databases for virtual screening

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.     

Collection and preparation of molecular databases for virtual screening

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.     

The multiple roles of computational chemistry in fragment-based drug design

Fragment-based drug discovery (FBDD) represents a change in strategy from the screening of molecules with higher molecular weights and physical properties more akin to fully drug-like compounds, to the screening of smaller, less complex molecules. This is because it has been recognised that fragment hit molecules can be efficiently grown and optimised into leads, particularly after the binding mode to the target protein has been first determined by 3D structural elucidation, e.g. by NMR or X-ray crystallography. Several studies have shown that medicinal chemistry optimisation of an already drug-like hit or lead compound can result in a final compound with too high molecular weight and lipophilicity. The evolution of a lower molecular weight fragment hit therefore represents an attractive alternative approach to optimisation as it allows better control of compound properties. Computational chemistry can play an important role both prior to a fragment screen, in producing a target focussed fragment library, and post-screening in the evolution of a drug-like molecule from a fragment hit, both with and without the available fragment-target co-complex structure. We will review many of the current developments in the area and illustrate with some recent examples from successful FBDD discovery projects that we have conducted.     

Prediction of new chromene-based inhibitors of tubulin using structure-based virtual screening and molecular dynamics simulation methods

Multidrug resistance (MDR) is one of the serious problems in cancer research that causes failure in chemotherapy. Chromene-based compounds have been proven to be the novel anti-MDR agents for inhibiting proliferation of tumor cells through tubulin polymerization inhibition of by binding at the colchicine binding site. In this study, we screened a chromene-based database of small molecules using physicochemical, ADMET properties and molecular docking to identify potential hit compounds. In order to validate our hit compounds, molecular dynamics simulations and related analysis were carried out and the results suggest that our hit compounds (PubChem CIDs: 16814409, 17594471, 57367244 and 69899719) can prove to be potential inhibitors of tubulin. The in silico results show that the present hits, like colchicine, effectively suppressed the dynamic instability of microtubules and induced microtubule-depolymerization and cell cycle arrest.     

Consideration of molecular weight during compound selection in virtual target-based database screening

Virtual database screening allows for millions of chemical compounds to be computationally selected based on structural complimentary to known inhibitors or to a target binding site on a biological macromolecule. Compound selection in virtual database screening when targeting a biological macromolecule is typically based on the interaction energy between the chemical compound and the target macromolecule. In the present study it is shown that this approach is biased toward the selection of high molecular weight compounds due to the contribution of the compound size to the energy score. To account for molecular weight during energy based screening, we propose normalization strategies based on the total number of heavy atoms in the chemical compounds being screened. This approach is computationally efficient and produces molecular weight distributions of selected compounds that can be selected to be (1) lower than that of the original database used in the virtual screening, which may be desirable for selection of leadlike compounds or (2) similar to that of the original database, which may be desirable for the selection of drug-like compounds. By eliminating the bias in target-based database screening toward higher molecular weight compounds it is anticipated that the proposed procedure will enhance the success rate of computer-aided drug design.     

Identification of potential inhibitors against nuclear Dam1 complex subunit Ask1 of Candida albicans using virtual screening and MD simulations

Identification of hit compounds against specific target form the starting point for a drug discovery program. A consistent decline of new chemical entities (NCEs) in recent years prompted a challenge to explore newer approaches to discover potential hit compounds that in turn can be converted into leads, and ultimately drug with desired therapeutic efficacy. The vast amount of omics and activity data available in public databases offers an opportunity to identify novel targets and their potential inhibitors. State of the art in silico methods viz., clustering of compounds, virtual screening, molecular docking, MD simulations and MMPBSA calculations were employed in a pipeline to identify potential ‘hits’ against those targets as well whose structures, as of now, could only predict through threading approaches. In the present work, we have started from scratch, amino acid sequence of target and compounds retrieved from PubChem compound database, modeled it in such a way that led to the identification of possible inhibitors of Dam1 complex subunit Ask1 of Candida albicans. We also propose a ligand based binding site determination approach. We have identified potential inhibitors of Ask1 subunit of a Dam1 complex of C. albicans, which is required to prevent precocious spindle elongation in pre-mitotic phases. The proposed scheme may aid to find virtually potential inhibitors of other unique targets against candida.     

Investigation on the isoform selectivity of novel kinesin-like protein 1 (KIF11) inhibitor using chemical feature based pharmacophore,molecular docking,and quantum mechanical studies

Kinesin-like protein (KIF11) is a molecular motor protein that is essential in mitosis. Removal of KIF11 prevents centrosome migration and causes cell arrest in mitosis. KIF11 defects are linked to the disease of microcephaly, lymph edema or mental retardation. The human KIF11 protein has been actively studied for its role in mitosis and its potential as a therapeutic target for cancer treatment. Pharmacophore modeling, molecular docking and density functional theory approaches was employed to reveal the structural, chemical and electronic features essential for the development of small molecule inhibitor for KIF11. Hence we have developed chemical feature based pharmacophore models using Discovery Studio v 2.5 (DS). The best hypothesis (Hypo1) consisting of four chemical features (two hydrogen bond acceptor, one hydrophobic and one ring aromatic) has exhibited high correlation co-efficient of 0.9521, cost difference of 70.63 and low RMS value of 0.9475. This Hypo1 is cross validated by Cat Scramble method; test set and decoy set to prove its robustness, statistical significance and predictability respectively. The well validated Hypo1 was used as 3Dquery to perform virtual screening. The hits obtained from the virtual screening were subjected to various scrupulous drug-like filters such as Lipinski’s rule of five and ADMET properties. Finally, six hit compounds were identified based on the molecular interaction and its electronic properties. Our final lead compound could serve as a powerful tool for the discovery of potent inhibitor as KIF11 agonists.     

Pharmacophore model-aided virtual screening combined with comparative molecular docking and molecular dynamics for identification of marine natural products as SARS-CoV-2 papain-like protease inhibitors

Targeting SARS-CoV-2 papain-like protease using inhibitors is a suitable approach for inhibition of virus replication and dysregulation of host anti-viral immunity. Engaging all five binding sites far from the catalytic site of PLpro is essential for developing a potent inhibitor. We developed and validated a structure-based pharmacophore model with 9 features of a potent PLpro inhibitor. The pharmacophore model-aided virtual screening of the comprehensive marine natural product database predicted 66 initial hits. This hit library was downsized by filtration through a molecular weight filter of ≤ 500 g/mol. The 50 resultant hits were screened by comparative molecular docking using AutoDock and AutoDock Vina. Comparative molecular docking enables benchmarking docking and relieves the disparities in the search and scoring functions of docking engines. Both docking engines retrieved 3 same compounds at different positions in the top 1 % rank, hence consensus scoring was applied, through which CMNPD28766, aspergillipeptide F emerged as the best PLpro inhibitor. Aspergillipeptide F topped the 50-hit library with a pharmacophore-fit score of 75.916. Favorable binding interactions were predicted between aspergillipeptide F and PLpro similar to the native ligand XR8-24. Aspergillipeptide F was able to engage all the 5 binding sites including the newly discovered BL2 groove, site V. Molecular dynamics for quantification of Cα-atom movements of PLpro after ligand binding indicated that it exhibits highly correlated domain movements contributing to the low free energy of binding and a stable conformation. Thus, aspergillipeptide F is a promising candidate for pharmaceutical and clinical development as a potent SARS-CoV-2 PLpro inhibitor.     

Cheminformatics study of some indole compounds through QSAR modeling,ADME prediction,molecular docking,and molecular dynamic simulation to identify novel inhibitors of HCV NS5B protease

The hepatitis C virus is a viral disease that causes cirrhosis and hepatocellular carcinoma in the liver. Since the virus's discovery, significant therapeutic advances have been made. Notwithstanding, its upsurge necessitates the development of novel approaches to combating this disease. Recent studies have seen a rise in the value of plant compounds in the establishment of novel, efficient, and cost-efficient anti-HCV medications. These factors have motivated us to continuously search for novel, potent anti-HCV inhibitors, ideally with mechanisms different from those used in conventional medicine. Several filtrations processes including ADMET, molecular docking, and molecular dynamics (MD) simulation were used to choose potent hits from plant molecules deposited in the PubChem database. The binding kinetics were explored using MD simulation studies performed for 100 ns using GROMACS-2018.1. which was done to validate and supplement the findings of the virtual screening. The selected hits (with PubChem CID of 3,560,948 and 4,754,183) had preferable molecular traits that suggested they might work well as HCV inhibitors and are term as effective potential hits. The MD study confirmed the stability of the identified hit with a binding energy of ?111.724 ( ± 81.668) kj/mol and mechanisms different from the FDA-approved drug (sofosbuvir) used as a reference with a binding energy of ?106.132 ( ± 78.008) kj/mol. This approach could aid in the establishment and identification of novel inhibitors in pharmaceutical discovery. Experimental assessments could indeed ascertain as to if the identified molecule can be employed as an anti-HCV drug to address the ailment and confirm the reliability of our in-silico studies.     

In search of novel inhibitors of anti-cancer drug target fibroblast growth factor receptors: Insights from virtual screening,molecular docking,and molecular dynamics

Fibroblast growth factor receptors (FGFR) are an essential player in oncogenesis and tumor progression. LY2874455 was identified as a pan-FGFR inhibitor and has gone through phase I clinical trial. In the current study, virtual screening was conducted against the PubChem database using a pharmacophore model generated from the crystal structure of FGFR4 inhibited by LY2874455. PubChem 137300327 was identified as the most suitable compound from this screening. Later, molecular docking and molecular dynamics studies conducted with FGFRs corroborated the initial finding. Analysis of ADMET properties disclosed that LY2874455 and PubChem 137300327 share alike properties. Our study suggests that PubChem 137300327 is a potential pan-FGFR inhibitor and can be exploited to treat different cancers following validation in proper wet-lab experiments and study in animal cancer models. This compound also follows Lipinski’s rules and can be used as a lead compound to synthesize more effective anticancer compounds.     

Toward designing drug-like libraries: a novel computational approach for prediction of drug feasibility of compounds

Prediction of the degree of drug-like character in small molecules is of great industrial interest. The major barrier, however, is the lack of a definition for drug-like character. We used the concept of the multilevel chemical compatibility (MLCC) between a compound and a drug library as a measure of the drug-like character of a compound. The rationale is that the local chemical environment of each atom or group of atoms in a compound largely contributes to the stability, toxicity, and metabolism in vivo. A systematic comparison of the local environments within a compound and those within the existing drugs provides a basis for determining whether and how much a compound is drug-like. We applied the MLCC calculations to four test sets: top selling drugs, compounds under biological testing prior to the preclinical test, anticancer drugs, and compounds known to have poor drug-like character. The following conclusions were obtained: (1) A convergent number of unique local structure types were found in the analysis of the library of the existing drugs. It suggests that the current drug library contains about 80% of all the viable types; therefore, discovery of a drug with new local structures is only an event of relatively small probability. (2) The method is highly selective in discerning drug-like compounds: most of the top drugs are predicted to be drug-like, about one-quarter of the biological testing compounds are drug-like, and about one-fifth of the anticancer drugs are drug-like. (3) The method also correctly predicted that none of the known problematic compounds are drug-like. (4) The method is fast enough for computational screening of virtual combinatorial chemistry libraries and databases of available compounds.