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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AI-powered therapeutic target discovery

Trends in Pharmacological Sciences, Год журнала: 2023, Номер 44(9), С. 561 - 572

Опубликована: Июль 19, 2023

Disease modeling and target identification are the most crucial initial steps in drug discovery, influence probability of success at every step development. Traditional is a time-consuming process that takes years to decades usually starts an academic setting. Given its advantages analyzing large datasets intricate biological networks, artificial intelligence (AI) playing growing role modern identification. We review recent advances focusing on breakthroughs AI-driven therapeutic exploration. also discuss importance striking balance between novelty confidence selection. An increasing number AI-identified targets being validated through experiments several AI-derived drugs entering clinical trials; we highlight current limitations potential pathways for moving forward.

Язык: Английский

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Precision treatment in advanced hepatocellular carcinoma

Cancer Cell, Год журнала: 2024, Номер 42(2), С. 180 - 197

Опубликована: Фев. 1, 2024

The past decade has witnessed significant advances in the systemic treatment of advanced hepatocellular carcinoma (HCC). Nevertheless, newly developed strategies have not achieved universal success and HCC patients frequently exhibit therapeutic resistance to these therapies. Precision represents a paradigm shift cancer recent years. This approach utilizes unique molecular characteristics individual patient personalize modalities, aiming maximize efficacy while minimizing side effects. Although precision shown multiple types, its application remains infancy. In this review, we discuss key aspects HCC, including biomarkers, classifications, heterogeneity tumor microenvironment. We also propose future directions, ranging from revolutionizing current methodologies personalizing therapy through functional assays, which will accelerate next phase advancements area.

Язык: Английский

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Chemistry42: An AI-Driven Platform for Molecular Design and Optimization

Journal of Chemical Information and Modeling, Год журнала: 2023, Номер 63(3), С. 695 - 701

Опубликована: Фев. 2, 2023

Chemistry42 is a software platform for de novo small molecule design and optimization that integrates Artificial Intelligence (AI) techniques with computational medicinal chemistry methodologies. efficiently generates novel molecular structures optimized properties validated in both vitro vivo studies available through licensing or collaboration. the core component of Insilico Medicine's Pharma.ai drug discovery suite. also includes PandaOmics target multiomics data analysis, inClinico─a data-driven multimodal forecast clinical trial's probability success (PoS). In this paper, we demonstrate how can be used to find against DDR1 CDK20.

Язык: Английский

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A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models

Nature Biotechnology, Год журнала: 2024, Номер unknown

Опубликована: Март 8, 2024

Abstract Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as anti-fibrotic target using predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, small-molecule TNIK inhibitor, which exhibits desirable drug-like properties activity across different organs vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects addition its profile, validated multiple studies. Its safety tolerability well pharmacokinetics were randomized, double-blinded, placebo-controlled phase I trial (NCT05154240) involving 78 healthy participants. A separate China, CTR20221542, also demonstrated comparable pharmacokinetic profiles. This work was completed roughly 18 months from discovery preclinical candidate nomination demonstrates capabilities of our generative drug-discovery pipeline.

Язык: Английский

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Benchmarking Refined and Unrefined AlphaFold2 Structures for Hit Discovery

Journal of Chemical Information and Modeling, Год журнала: 2023, Номер 63(6), С. 1656 - 1667

Опубликована: Март 10, 2023

The recently developed AlphaFold2 (AF2) algorithm predicts proteins’ 3D structures from amino acid sequences. open AlphaFold protein structure database covers the complete human proteome. Using an industry-leading molecular docking method (Glide), we investigated virtual screening performance of 37 common drug targets, each with AF2 and known holo apo DUD-E data set. In a subset 27 targets where are suitable for refinement, show comparable early enrichment active compounds (avg. EF 1%: 13.0) to 11.4) while falling behind 24.2). With induced-fit protocol (IFD-MD), can refine using aligned binding ligand as template improve in structure-based 18.9). Glide-generated poses ligands also be used templates IFD-MD, achieving similar improvements 1% 18.0). Thus, proper preparation considerable promise silico hit identification.

Язык: Английский

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Self-Driving Laboratories for Chemistry and Materials Science

Chemical Reviews, Год журнала: 2024, Номер 124(16), С. 9633 - 9732

Опубликована: Авг. 13, 2024

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through automation experimental workflows, along with autonomous planning, SDLs hold potential to greatly accelerate research in chemistry and materials discovery. This review provides in-depth analysis state-of-the-art SDL technology, its applications across various disciplines, implications for industry. additionally overview enabling technologies SDLs, including their hardware, software, integration laboratory infrastructure. Most importantly, this explores diverse range domains where have made significant contributions, from drug discovery science genomics chemistry. We provide a comprehensive existing real-world examples different levels automation, challenges limitations associated each domain.

Язык: Английский

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Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice

Journal of the American College of Cardiology, Год журнала: 2024, Номер 84(1), С. 97 - 114

Опубликована: Июнь 24, 2024

Язык: Английский

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AI is a viable alternative to high throughput screening: a 318-target study

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Апрель 2, 2024

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand libraries can access far greater space, provided that the predictive accuracy sufficient useful Through largest and most diverse virtual HTS campaign reported date, comprising 318 individual projects, we demonstrate our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area protein class. We address historical limitations computational by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking compounds. show molecules selected model are drug-like scaffolds rather than minor modifications Our empirical results suggest methods substantially replace as first step small-molecule drug discovery.

Язык: Английский

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