Artificial intelligence for natural product drug discovery

Nature Reviews Drug Discovery, Journal Year: 2023, Volume and Issue: 22(11), P. 895 - 916

Published: Sept. 11, 2023

Language: Английский

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Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

Nature Reviews Drug Discovery, Journal Year: 2023, Volume and Issue: 23(2), P. 141 - 155

Published: Dec. 8, 2023

Language: Английский

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Can language models be used for real-world urban-delivery route optimization?

The Innovation, Journal Year: 2023, Volume and Issue: 4(6), P. 100520 - 100520

Published: Sept. 30, 2023

Language models have contributed to breakthroughs in interdisciplinary research, such as protein design and molecular dynamics understanding. In this study, we reveal that beyond language, representations of other entities, human behaviors, are mappable learnable sequences can be learned by language models. One compelling example is the real-world delivery route optimization problem. We here propose a novel approach based on model optimize routes basis drivers' historical experiences. Although broad range optimization-based approaches been designed routes, they do not capture implicit knowledge complex operating environments. The integrates process learning from driving behaviors experienced drivers. A preserves behavioral patterns first analogized sentence natural language. Through unsupervised learning, then learn vector words infer chains tailored chain-reaction-based algorithm. also provide insights into fusion operations research methods. our approach, applied new deliveries at zone level, while classic traveling salesman problem (TSP) embedded hybrid framework for intra-zone optimization. Numerical experiments performed data Amazon's service demonstrate proposed outperforms pure optimization, supporting effectiveness, efficiency, extensibility model. As versatile easily extended various disciplines which follow certain grammar rules. anticipate work will serve stepping stone toward understanding application tackling problems.

Language: Английский

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Structure-based drug design with geometric deep learning

Current Opinion in Structural Biology, Journal Year: 2023, Volume and Issue: 79, P. 102548 - 102548

Published: Feb. 25, 2023

Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept neural-network-based machine has been applied macromolecular structures. This review provides overview the recent applications learning in bioorganic and medicinal chemistry, highlighting its potential for structure-based discovery design. Emphasis is placed on molecular property prediction, ligand binding site pose de novo The current challenges opportunities are highlighted, a forecast future presented.

Language: Английский

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Machine learning in preclinical drug discovery

Nature Chemical Biology, Journal Year: 2024, Volume and Issue: 20(8), P. 960 - 973

Published: July 19, 2024

Language: Английский

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Prospective de novo drug design with deep interactome learning

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: April 22, 2024

Abstract De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- structure-based generation of drug-like molecules. This method capitalizes on the unique strengths both graph neural networks language models, offering an alternative need application-specific reinforcement, transfer, or few-shot learning. It enables “zero-shot" construction compound libraries tailored bioactivity, synthesizability, structural novelty. In order proactively evaluate interactome framework protein design, potential new ligands targeting binding site human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs chemically synthesized computationally, biophysically, biochemically characterized. Potent PPAR partial agonists identified, demonstrating favorable activity desired selectivity profiles nuclear receptors off-target interactions. Crystal structure determination ligand-receptor complex confirms anticipated mode. successful outcome positively advocates de application in bioorganic medicinal chemistry, enabling creation innovative bioactive

Language: Английский

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Machine learning-aided generative molecular design

Nature Machine Intelligence, Journal Year: 2024, Volume and Issue: 6(6), P. 589 - 604

Published: June 18, 2024

Language: Английский

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PocketFlow is a data-and-knowledge-driven structure-based molecular generative model

Nature Machine Intelligence, Journal Year: 2024, Volume and Issue: 6(3), P. 326 - 337

Published: March 11, 2024

Language: Английский

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Knowledge Management Perspective of Generative Artificial Intelligence

Journal of the Association for Information Systems, Journal Year: 2024, Volume and Issue: 25(1), P. 1 - 12

Published: Jan. 1, 2024

In this editorial, revisiting Alavi and Leidner (2001) as a conceptual lens, we consider the organizational implications of Generative Artificial Intelligence (GenAI) from knowledge management (KM) perspective. We examine how GenAI impact processes creation, storage, transfer, application, highlighting both opportunities challenges technology presents. enhances information? processing cognitive functions, fostering individual learning. However, it also introduces risks like AI bias reduced human socialization, potentially marginalizing junior workers. For storage retrieval, GenAI’s ability to quickly access vast bases significantly changes employee interactions with KM systems. This raises questions about balancing human-derived tacit AI-generated explicit knowledge. The paper explores role in particularly training cultivating learning culture. Challenges include an over-reliance on disseminating sensitive information. terms is seen tool boost productivity innovation, but issues misapplication, intellectual property, ethical considerations are critical. Conclusively, argues for balanced approach integrating into processes. It advocates harmonizing capabilities insights effectively manage contemporary organizations, ensuring technological advances responsibility.

Language: Английский

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Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities

Frontiers in Pharmacology, Journal Year: 2024, Volume and Issue: 15

Published: Feb. 7, 2024

There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules commercially successful drugs through quantitative structure-activity relationships and virtual screening. second approach generating new de novo inverse relationship. Both methods aim get a molecule with the best pharmacokinetic pharmacodynamic profiles. However, bringing market is an expensive time-consuming endeavor, average cost being estimated at around $2.5 billion. One of biggest challenges screening vast number potential candidates find one that both safe effective. development artificial intelligence in recent years has been phenomenal, ushering revolution many fields. field pharmaceutical sciences also significantly benefited from multiple applications intelligence, especially discovery projects. Artificial models finding use molecular property prediction, generation, screening, synthesis planning, repurposing, among others. Lately, generative gained popularity across domains for its ability generate entirely data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative delivered promising results development. This review article delves into fundamentals framework various context via approach. Various basic advanced have discussed, along their applications. explores examples advances approach, well ongoing efforts fully harness faster more affordable manner. Some clinical-level assets generated form discussed this show ever-increasing application commercial partnerships.

Language: Английский

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