Computational approaches streamlining drug discovery

Nature, Год журнала: 2023, Номер 616(7958), С. 673 - 685

Опубликована: Апрель 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.

Язык: Английский

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AlphaFold2 and its applications in the fields of biology and medicine

Signal Transduction and Targeted Therapy, Год журнала: 2023, Номер 8(1)

Опубликована: Март 14, 2023

Abstract AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction one the most challenging problems in computational biology and chemistry, has puzzled scientists for 50 years. The advent AF2 presents unprecedented progress protein attracted much attention. Subsequent release more than 200 million predicted further aroused great enthusiasm science community, especially fields medicine. thought to have a significant impact on structural research areas need information, such as drug discovery, design, function, et al. Though time not long since was developed, there are already quite few application studies medicine, many them having preliminarily proved potential AF2. To better understand promote its applications, we will this article summarize principle architecture well recipe success, particularly focus reviewing applications Limitations current also be discussed.

Язык: Английский

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How good are AlphaFold models for docking-based virtual screening?

iScience, Год журнала: 2022, Номер 26(1), С. 105920 - 105920

Опубликована: Дек. 30, 2022

A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures protein target. Whenever experimental were not available, homology modeling has been, so far, method choice. Recently, AlphaFold (AF), an artificial-intelligence-based structure prediction method, shown impressive results terms model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from perspective docking-based discovery. We compared high-throughput docking (HTD) performance with their corresponding PDB using a benchmark set 22 targets. The showed consistently worse four programs and two consensus techniques. Although shows remarkable ability predict architecture, this might be enough guarantee that can reliably used for HTD, post-modeling refinement strategies key increase chances success.

Язык: Английский

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Modeling conformational states of proteins with AlphaFold

Current Opinion in Structural Biology, Год журнала: 2023, Номер 81, С. 102645 - 102645

Опубликована: Июнь 29, 2023

Язык: Английский

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The Art and Science of Molecular Docking

Annual Review of Biochemistry, Год журнала: 2024, Номер 93(1), С. 389 - 410

Опубликована: Апрель 10, 2024

Molecular docking has become an essential part of a structural biologist's and medicinal chemist's toolkits. Given chemical compound the three-dimensional structure molecular target—for example, protein—docking methods fit into target, predicting compound's bound binding energy. Docking can be used to discover novel ligands for target by screening large virtual libraries. also provide useful starting point structure-based ligand optimization or investigating ligand's mechanism action. Advances in computational methods, including both physics-based machine learning approaches, as well complementary experimental techniques, are making even more powerful tool. We review how works it drive drug discovery biological research. describe its current limitations ongoing efforts overcome them.

Язык: Английский

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State-specific protein–ligand complex structure prediction with a multiscale deep generative model

Nature Machine Intelligence, Год журнала: 2024, Номер 6(2), С. 195 - 208

Опубликована: Фев. 12, 2024

Язык: Английский

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AlphaFold2 structures guide prospective ligand discovery

Science, Год журнала: 2024, Номер 384(6702)

Опубликована: Май 16, 2024

AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 of the σ

Язык: Английский

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AlphaFold, allosteric, and orthosteric drug discovery: Ways forward

Drug Discovery Today, Год журнала: 2023, Номер 28(6), С. 103551 - 103551

Опубликована: Март 11, 2023

Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version buttressed by an innovative machine-learning approach that integrates physical biological knowledge about protein structures, raised drug hopes unsurprisingly, have not come to bear. Even though accurate, models are rigid, including pockets. AlphaFold's mixed performance poses question how its power can be harnessed in discovery. Here we discuss possible ways going forward wielding strengths, while bearing mind what AlphaFold cannot do. For kinases receptors, input enriched active (ON) state better chance rational design success.

Язык: Английский

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AI for targeted polypharmacology: The next frontier in drug discovery

Current Opinion in Structural Biology, Год журнала: 2024, Номер 84, С. 102771 - 102771

Опубликована: Янв. 11, 2024

Язык: Английский

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Structure-based virtual screening of vast chemical space as a starting point for drug discovery

Current Opinion in Structural Biology, Год журнала: 2024, Номер 87, С. 102829 - 102829

Опубликована: Июнь 6, 2024

Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge commercially available chemical space provides the opportunity search for ligands therapeutic targets among billions compounds. This review offers compact overview structure-based screens vast spaces, highlighting successful applications in early drug discovery therapeutically important such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies explore ultra-large libraries synergies emerging machine learning techniques. current opportunities future challenges are discussed, indicating that this approach will play an role next-generation pipeline.

Язык: Английский

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