Virtual screening (VS) is an integral and ever‐evolving domain of drug discovery framework. The VS is traditionally classified into ligand‐based (LB) and structure‐based (SB) approaches. Machine ...intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as “big‐data” in the public domain demands modern and advanced machine intelligence‐driven VS approaches to screen hit molecules from ultra‐large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big‐data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.
Methods for the pairwise comparison of 2D and 3D molecular structures are established approaches in virtual screening. In this work, we explored three strategies for maximizing the virtual screening ...performance of these methods: (i) the merging of hit lists obtained from multi-compound screening using a single screening method, (ii) the merging of the hit lists obtained from 2D and 3D screening by parallel selection, and (iii) the combination of both of these strategies in an integrated approach. We found that any of these strategies led to a boost in virtual screening performance, with the clearest advantages observed for the integrated approach. On test sets for virtual screening, covering 50 pharmaceutically relevant proteins, the integrated approach, using sets of five query molecules, yielded, on average, an area under the receiver operating characteristic curve (AUC) of 0.84, an early enrichment among the top 1% of ranked compounds (EF1%) of 53.82 and a scaffold recovery rate among the top 1% of ranked compounds (SRR1%) of 0.50. In comparison, the 2D and 3D methods on their own (when using a single query molecule) yielded AUC values of 0.68 and 0.54, EF1% values of 19.96 and 17.52, and SRR1% values of 0.20 and 0.17, respectively. In conclusion, based on these results, the integration of 2D and 3D methods, via a (balanced) parallel selection strategy, is recommended, and, in particular, when combined with multi-query screening.
Chronic pain is a common and challenging clinical problem that significantly impacts patients' quality of life. The sodium channel Nav1.8 plays a crucial role in the occurrence and development of ...chronic pain, making it one of the key targets for treating chronic pain. In this article, we combined virtual screening with cell membrane chromatography techniques to establish a novel method for rapid high-throughput screening of selective Nav1.8 inhibitors. Using this approach, we identified a small molecule compound 6, which not only demonstrated high affinity and inhibitory activity against Nav1.8 but also exhibited significant inhibitory effects on CFA-induced chronic inflammatory pain. Compared to the positive drug VX-150, compound 6 showed a more prolonged analgesic effect making it a promising candidate as a Nav1.8 inhibitor with potential clinical applications. This discovery provides a new therapeutic option for the treatment of chronic pain.Chronic pain is a common and challenging clinical problem that significantly impacts patients' quality of life. The sodium channel Nav1.8 plays a crucial role in the occurrence and development of chronic pain, making it one of the key targets for treating chronic pain. In this article, we combined virtual screening with cell membrane chromatography techniques to establish a novel method for rapid high-throughput screening of selective Nav1.8 inhibitors. Using this approach, we identified a small molecule compound 6, which not only demonstrated high affinity and inhibitory activity against Nav1.8 but also exhibited significant inhibitory effects on CFA-induced chronic inflammatory pain. Compared to the positive drug VX-150, compound 6 showed a more prolonged analgesic effect making it a promising candidate as a Nav1.8 inhibitor with potential clinical applications. This discovery provides a new therapeutic option for the treatment of chronic pain.
Recently, the world has witnessed outbreak of a novel Coronavirus (SARS-CoV-2), the virus which initially emerged in Wuhan, China has now made its way to a large part of the world, resulting in a ...public emergency of international concern. The functional importance of Chymotrypsin-like protease (3CL
pro
) in viral replication and maturation turns it into an attractive target for the development of effective antiviral drugs against SARS and other coronaviruses. At present, there is no standard drug regime nor any vaccine available against the infection. The rapid development and identification of efficient interventions against SARS-CoV-2 remains a major challenge. Based on the available knowledge of closely related coronavirus and their safety profiles, repurposing of existing antiviral drugs and screening of available databases is considered a near term strategic and economic way to contain the SARS-CoV-2 pandemic. Herein, we applied computational drug design methods to identify Chymotrypsin-like protease inhibitors from FDA approved antiviral drugs and our in-house database of natural and drug-like compounds of synthetic origin. As a result three FDA approved drugs (Remdesivir, Saquinavir and Darunavir) and two natural compounds (. flavone and coumarine derivatives) were identified as promising hits. Further, MD simulation and binding free energy calculations were performed to evaluate the dynamic behavior, stability of protein-ligand contact, and binding affinity of the hit compounds. Our results indicate that the identified compounds can inhibit the function of Chymotrypsin-like protease (3CL
pro
) of Coronavirus. Considering the severity of the spread of coronavirus, the current study is in-line with the concept of finding the new inhibitors against the vital pathway of the corona virus to expedite the process of drug discovery.
Communicated by Ramaswamy H. Sarma
The enzyme 15-hydroxyprostaglandin dehydrogenase (15-PGDH), which acts as a negative regulator of prostaglandin E2 (PGE2) levels and activity, represents a promising pharmacological target for ...promoting liver regeneration. In this study, we collected data on 15-PGDH homologous family proteins, their inhibitors, and traditional Chinese medicine (TCM) compounds. Leveraging machine learning and molecular docking techniques, we constructed a prediction model for virtual screening of 15-PGDH inhibitors from TCM compound library and successfully screened genistein as a potential 15-PGDH inhibitor. Through further validation, it was discovered that genistein considerably enhances liver regeneration by inhibiting 15-PGDH, resulting in a significant increase in the PGE2 level. Genistein's effectiveness suggests its potential as a novel therapeutic agent for liver diseases, highlighting this study's contribution to expanding the clinical applications of TCM.
•A Support Vector Machines (SVM)-based prediction model for 15-PGDH inhibitors was developed.•A promising candidate 15-PGDH inhibitor was predicted by the SVM-based model and identified through molecular docking.•This candidate 15-PGDH inhibitor exhibited superior efficacy in promoting liver regeneration.
Virtual screening (VS) is a powerful technique for identifying hit molecules as starting points for medicinal chemistry. The number of methods and softwares which use the ligand and target-based VS ...approaches is increasing at a rapid pace. What, however, are the real advantages and disadvantages of the VS technology and how applicable is it to drug discovery projects? This review provides a comprehensive appraisal of several VS approaches currently available. In the first part of this work, an overview of the recent progress and advances in both ligand-based VS (LBVS) and structure-based VS (SBVS) strategies highlighting current problems and limitations will be provided. Special emphasis will be given to in silico chemogenomics approaches which utilize annotated ligand-target as well as protein-ligand interaction databases and which could predict or reveal promiscuous binding and polypharmacology, the knowledge of which would help medicinal chemists to design more potent clinical candidates with fewer side effects. In the second part, recent case studies (all published in the last two years) will be discussed where the VS technology has been applied successfully. A critical analysis of these case studies provides a good platform in order to estimate the applicability of various VS strategies in the new lead identification and optimization.
We present a new computational approach, named Watermelon, designed for the development of pharmacophore models based on receptor structures. The methodology involves the sampling of potential ...hotspots for ligand interactions within a protein target’s binding site, utilising molecular fragments as probes. By employing docking and molecular dynamics (MD) simulations, the most significant interactions formed by these probes within distinct regions of the binding site are identified. These interactions are subsequently transformed into pharmacophore features that delineates key anchoring sites for potential ligands. The reliability of the approach was experimentally validated using the monoacylglycerol lipase (MAGL) enzyme. The generated pharmacophore model captured features representing ligand-MAGL interactions observed in various X-ray co-crystal structures and was employed to screen a database of commercially available compounds, in combination with consensus docking and MD simulations. The screening successfully identified two new MAGL inhibitors with micromolar potency, thus confirming the reliability of the Watermelon approach.
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