Computational approaches streamlining drug discovery

Nature, Journal Year: 2023, Volume and Issue: 616(7958), P. 673 - 685

Published: April 26, 2023

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This is largely defined by flood of data on ligand properties binding to therapeutic targets their 3D structures, abundant computing capacities advent on-demand virtual libraries drug-like small molecules billions. Taking full advantage these resources requires fast methods effective screening. includes structure-based screening gigascale chemical spaces, further facilitated iterative approaches. Highly synergistic are developments deep learning predictions target activities lieu receptor structure. Here we review recent advances technologies, potential reshaping whole process development, as well challenges they encounter. We also discuss how rapid identification highly diverse, potent, target-selective ligands protein can democratize process, presenting new opportunities cost-effective development safer more small-molecule treatments. Recent approaches application streamlining discussed.

Language: Английский

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Efficient Exploration of Chemical Space with Docking and Deep Learning

Journal of Chemical Theory and Computation, Journal Year: 2021, Volume and Issue: 17(11), P. 7106 - 7119

Published: Sept. 30, 2021

With the advent of make-on-demand commercial libraries, number purchasable compounds available for virtual screening and assay has grown explosively in recent years, with several libraries eclipsing one billion compounds. Today's are larger more diverse, enabling discovery more-potent hit unlocking new areas chemical space, represented by core scaffolds. Applying physics-based silico methods an exhaustive manner, where every molecule library must be enumerated evaluated independently, is increasingly cost-prohibitive. Here, we introduce a protocol machine learning-enhanced molecular docking based on active learning to dramatically increase throughput over traditional docking. We leverage novel selection that strikes balance between two objectives: (1) identifying best scoring (2) exploring large region demonstrating superior performance compared purely greedy approach. Together automated redocking top compounds, this method captures almost all high scaffolds found This applied our campaigns against D4 AMPC targets produced dozens highly potent, inhibitors, blind test MT1 target. Our recovers than 80% experimentally confirmed hits 14-fold reduction compute cost, 90% 5% model predictions, preserving diversity

Language: Английский

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Ultralarge Virtual Screening Identifies SARS-CoV-2 Main Protease Inhibitors with Broad-Spectrum Activity against Coronaviruses

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 144(7), P. 2905 - 2920

Published: Feb. 10, 2022

Drugs targeting SARS-CoV-2 could have saved millions of lives during the COVID-19 pandemic, and it is now crucial to develop inhibitors coronavirus replication in preparation for future outbreaks. We explored two virtual screening strategies find main protease ultralarge chemical libraries. First, structure-based docking was used screen a diverse library 235 million compounds against active site. One hundred top-ranked were tested binding enzymatic assays. Second, fragment discovered by crystallographic optimized guided elaborated molecules experimental testing 93 compounds. Three identified first screen, five selected elaborations showed inhibitory effects. Crystal structures target-inhibitor complexes confirmed predictions hit-to-lead optimization, resulting noncovalent inhibitor with nanomolar affinity, promising vitro pharmacokinetic profile, broad-spectrum antiviral effect infected cells.

Language: Английский

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Serine 477 plays a crucial role in the interaction of the SARS-CoV-2 spike protein with the human receptor ACE2

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: Feb. 22, 2021

Since the worldwide outbreak of infectious disease COVID-19, several studies have been published to understand structural mechanism novel coronavirus SARS-CoV-2. During infection process, SARS-CoV-2 spike (S) protein plays a crucial role in receptor recognition and cell membrane fusion process by interacting with human angiotensin-converting enzyme 2 (hACE2) receptor. However, new variants these proteins emerge as virus passes through reservoir. This poses major challenge for designing potent antigen an effective immune response against protein. Through normal mode analysis (NMA) we identified highly flexible region binding domain (RBD) SARS-CoV-2, starting from residue 475 up 485. Structurally, position S477 shows highest flexibility among them. At same time, is hitherto most frequently exchanged amino acid RBDs mutants. Therefore, using MD simulations, investigated its two frequent mutations (S477G S477N) at RBD during hACE2. We found that exchanges S477G S477N strengthen SARS-COV-2 hACE2

Language: Английский

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Molecular Docking: Principles, Advances, and Its Applications in Drug Discovery

Letters in Drug Design & Discovery, Journal Year: 2022, Volume and Issue: 21(3), P. 480 - 495

Published: Sept. 23, 2022

Abstract: Molecular docking is a structure-based computational method that generates the binding pose and affinity between ligands targets. There are many powerful programs. However, there no single program suitable for every system. Hence, an appropriate chosen based on availability, need, computer capacity. has clear steps should be followed carefully to get good result. : applications at various stages in drug discovery. Although it application areas, commonly applied virtual screening repurposing. As result, playing substantial role endeavor discover potent against COVID-19. also approved drugs pharmaceutical market developed through use of molecular docking. accessible data increasing advancing with contribution latest developments, its discovery increasing. played crucial making faster, cheaper, more effective. More advances algorithms, integration other methods, introduction new approaches expected. Thus, will make easier

Language: Английский

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GROMACS in the Cloud: A Global Supercomputer to Speed Up Alchemical Drug Design

Journal of Chemical Information and Modeling, Journal Year: 2022, Volume and Issue: 62(7), P. 1691 - 1711

Published: March 30, 2022

We assess costs and efficiency of state-of-the-art high-performance cloud computing compare the results to traditional on-premises compute clusters. Our use case is atomistic simulations carried out with GROMACS molecular dynamics (MD) toolkit a particular focus on alchemical protein-ligand binding free energy calculations. set up cluster in Amazon Web Services (AWS) that incorporates various different instances Intel, AMD, ARM CPUs, some GPU acceleration. Using representative biomolecular simulation systems, we benchmark how performs individual across multiple instances. Thereby which deliver highest performance are most cost-efficient ones for our case. find that, terms total costs, including hardware, personnel, room, energy, cooling, producing MD trajectories can be about as an given optimal chosen. Further, high-throughput ligand-screening accelerated dramatically by using global resources. For ligand screening study consisting 19 872 independent or ∼200 μs combined trajectory, made diverse hardware available at time study. The computations scaled-up reach peak more than 4 000 instances, 140 cores, 3 GPUs simultaneously. ensemble finished 2 days cloud, while weeks would required complete task typical several hundred nodes.

Language: Английский

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Antiviral Drug Discovery for the Treatment of COVID-19 Infections

Viruses, Journal Year: 2022, Volume and Issue: 14(5), P. 961 - 961

Published: May 4, 2022

The coronavirus disease 2019 (COVID-19) pandemic is caused by the severe acute respiratory syndrome 2 (SARS-CoV-2), a recently emerged human coronavirus. COVID-19 vaccines have proven to be successful in protecting vaccinated from infection, reducing severity of disease, and deterring transmission infection. However, vaccination faces many challenges, such as decline vaccine-induced immunity over time, decrease potency against some SARS-CoV-2 variants including Omicron variant, resulting breakthrough infections. challenges that facing highlight importance discovery antivirals serve another means tackle pandemic. To date, neutralizing antibodies block viral entry targeting spike protein make up largest class has received US FDA emergency use authorization (EUA) for treatment. In addition protein, other key targets direct-acting include enzymes are essential replication, RNA-dependent RNA polymerase proteases, judged approval remdesivir, EUA Paxlovid (nirmatrelvir + ritonavir) treating This review presents an overview current status future direction antiviral drug infections, covering important non-structural (nsp) 3 papain-like protease, nsp5 main nsp12/nsp7/nsp8 complex.

Language: Английский

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The impact of library size and scale of testing on virtual screening

Nature Chemical Biology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Language: Английский

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Biomolecular modeling thrives in the age of technology

Nature Computational Science, Journal Year: 2021, Volume and Issue: 1(5), P. 321 - 331

Published: May 20, 2021

The biomolecular modeling field has flourished since its early days in the 1970s due to rapid adaptation and tailoring of state-of-the-art technology. resulting dramatic increase size timespan simulations outpaced Moore's law. Here, we discuss role knowledge-based versus physics-based methods hardware software advances propelling forward. This outreach suggests a bright future for modeling, where theory, experimentation simulation define three pillars needed address scientific biomedical challenges.

Language: Английский

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High-Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Noncovalent Inhibitor

Journal of Chemical Information and Modeling, Journal Year: 2021, Volume and Issue: 62(1), P. 116 - 128

Published: Nov. 18, 2021

Despite the recent availability of vaccines against acute respiratory syndrome coronavirus 2 (SARS-CoV-2), search for inhibitory therapeutic agents has assumed importance especially in context emerging new viral variants. In this paper, we describe discovery a novel noncovalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits SARS-Cov-2 main protease (Mpro) by employing scalable high-throughput virtual screening (HTVS) framework targeted compound library over 6.5 million molecules could be readily ordered purchased. Our HTVS leverages U.S. supercomputing infrastructure achieving nearly 91% resource utilization 126 docking calculations per hour. Downstream biochemical assays validate Mpro inhibitor with an inhibition constant (Ki) 2.9 μM (95% CI 2.2, 4.0). Furthermore, using room-temperature X-ray crystallography, show MCULE-5948770040 cleft primary binding site forming stable hydrogen bond hydrophobic interactions. We then used multiple μs-time scale molecular dynamics (MD) simulations machine learning (ML) techniques elucidate how bound ligand alters conformational states accessed Mpro, involving motions both proximal distal site. Together, our results demonstrate offers springboard further design.

Language: Английский

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