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.
GSPT1 plays crucial physiological functions, such as terminating protein translation, overexpressed in various tumors. It is a promising anti-tumor target, but is also considered as an “undruggable” ...protein. Recent studies have found that a class of small molecules can degrade GSPT1 through the “molecular glue” mechanism with strong antitumor activity, which is expected to become a new therapy for hematological malignancies. Currently available GSPT1 degraders are mostly derived from the scaffold of immunomodulatory imide drug (IMiD), thus more active compounds with novel structure remain to be found. In this work, using computer-assisted multi-round virtual screening and bioassay, we identified a non-IMiD acylhydrazone compound, AN5782, which can reduce the protein level of GPST1 and obviously inhibit the proliferation of tumor cells. Some analogs were obtained by a substructure search of AN5782. The structure-activity relationship analysis revealed possible interactions between these compounds and CRBN-GSPT1. Further biological mechanistic studies showed that AN5777 decreased GSPT1 remarkably through the ubiquitin-proteasome system, and its effective cytotoxicity was CRBN- and GSPT1-dependent. Furthermore, AN5777 displayed good antiproliferative activities against U937 and OCI-AML-2 cells, and dose-dependently induced G1 phase arrest and apoptosis. The structure found in this work could be good start for antitumor drug development.
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•A novel GSPT1 degrader was identified by virtual screening and bioassay.•AN5777 binds recombinant CRBN and induces GSPT1 degradation through the UPS.•The proliferation inhibition of AN5777 depends on CRBN and GSPT1.•AN5777 dose-dependently induces G1 phase arrest and apoptosis.
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
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.
•A potencial SSRI (18a) was designed rationally by applying both an artificial neural Network-based QSAR model and molecular docking.•Organic synthesis was achieved in high yields without loss of ...stereoselectivity.•Paroxetine exhibited hemolytic effects at 2.3, 1.29 y 0.67 mM, while 18a didn't cause hemolysis at any of the concentrations tested.
Depression is one of the most common mental illnesses, affecting almost 300 million people. According to the WHO, depression is one of the world's leading causes of disability and morbidity. People with this illness require both psychological and pharmaceutical treatment because severe depressive episodes often result in suicide. Selective serotonin reuptake inhibitors (SSRI) are widely used antidepressants that target the human serotonin transporter (hSERT). The crystallization of hSERT and the experimental data available allows cost and time-efficient computational tools like virtual screening (VS) to be utilized in the development of therapeutic agents. Here, we synthesized, characterized, and evaluated the biological activity of a novel SSRI analog of paroxetine, rationally designed by applying an artificial neural network-based QSAR model and a molecular docking analysis on hSERT. The analog N-substituted 18a showed higher affinity for the transporter (-10.2 kcal/mol), lower Ki value (1.19 nM) and a safer toxicological profile than paroxetine and was synthesized with a 71% yield. The in vitro cytotoxicity of the analog was evaluated using human glioblastoma (U87 MG), human neuroblastoma (SH SY5Y) and murine fibroblast (L929) cell lines. Also, the hemolytic ability of the compound was assessed on human erythrocytes. Results showed that analog 18a did not exhibit cytotoxic activity on the cell lines used and has no hemolytic activity at any of the concentrations tested, whereas with paroxetine, hemolysis was observed at 2.3, 1.29 y 0.67 mM. Based on these results, it is possible to suggest that analog 18a could be a promising new SSRI candidate for the treatment of this illness.
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Advancing Drug Discovery via Artificial Intelligence Chan, H.C. Stephen; Shan, Hanbin; Dahoun, Thamani ...
Trends in pharmacological sciences (Regular ed.),
August 2019, 2019-08-00, 20190801, Volume:
40, Issue:
8
Journal Article
Peer reviewed
Drug discovery and development are among the most important translational science activities that contribute to human health and wellbeing. However, the development of a new drug is a very complex, ...expensive, and long process which typically costs 2.6 billion USD and takes 12 years on average. How to decrease the costs and speed up new drug discovery has become a challenging and urgent question in industry. Artificial intelligence (AI) combined with new experimental technologies is expected to make the hunt for new pharmaceuticals quicker, cheaper, and more effective. We discuss here emerging applications of AI to improve the drug discovery process.
AI has enormous potential to revolutionize drug discovery.Computational prediction of atomic and molecular properties is the foundation of most de novo design strategies.Machine learning, a branch of AI, can now predict the physical and chemical properties of small molecules at quantum mechanics-level accuracy with much lower time-cost.AI is also able to search for correlations between molecular representations and biological and toxicological activities.AI-based algorithms are also being developed to efficiently probe the pathways of synthesis of novel drug candidates.In combination with robotic platforms, the chemical space for novel reactions can be explored by learning from automated analysis of reaction feasibility.
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