Artificial intelligence in drug discovery and development

Drug Discovery Today, Journal Year: 2020, Volume and Issue: 26(1), P. 80 - 93

Published: Oct. 21, 2020

Language: Английский

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A mobile robotic chemist

Nature, Journal Year: 2020, Volume and Issue: 583(7815), P. 237 - 241

Published: July 8, 2020

Language: Английский

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Artificial intelligence: A powerful paradigm for scientific research

The Innovation, Journal Year: 2021, Volume and Issue: 2(4), P. 100179 - 100179

Published: Oct. 29, 2021

•"Can machines think?" The goal of artificial intelligence (AI) is to enable mimic human thoughts and behaviors, including learning, reasoning, predicting, so on.•"Can AI do fundamental research?" coupled with machine learning techniques impacting a wide range sciences, mathematics, medical science, physics, etc.•"How does accelerate New research applications are emerging rapidly the support by infrastructure, data storage, computing power, algorithms, frameworks. Artificial promising (ML) well known from computer science broadly affecting many aspects various fields technology, industry, even our day-to-day life. ML have been developed analyze high-throughput view obtaining useful insights, categorizing, making evidence-based decisions in novel ways, which will promote growth fuel sustainable booming AI. This paper undertakes comprehensive survey on development application different information materials geoscience, life chemistry. challenges that each discipline meets, potentials handle these challenges, discussed detail. Moreover, we shed light new trends entailing integration into scientific discipline. aim this provide broad guideline sciences potential infusion AI, help motivate researchers deeply understand state-of-the-art AI-based thereby continuous sciences.

Language: Английский

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A robotic platform for flow synthesis of organic compounds informed by AI planning

Science, Journal Year: 2019, Volume and Issue: 365(6453)

Published: Aug. 8, 2019

Pairing prediction and robotic synthesis Progress in automated of organic compounds has been proceeding along parallel tracks. One goal is algorithmic viable routes to a desired compound; the other implementation known reaction sequence on platform that needs little no human intervention. Coley et al. now report preliminary integration these two protocols. They paired retrosynthesis algorithm with robotically reconfigurable flow apparatus. Human intervention was still required supplement predictor practical considerations such as solvent choice precise stoichiometry, although predictions should improve accessible data accumulate for training. Science , this issue p. eaax1566

Language: Английский

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Rethinking drug design in the artificial intelligence era

Nature Reviews Drug Discovery, Journal Year: 2019, Volume and Issue: 19(5), P. 353 - 364

Published: Dec. 4, 2019

Language: Английский

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Advancing Drug Discovery via Artificial Intelligence

Trends in Pharmacological Sciences, Journal Year: 2019, Volume and Issue: 40(8), P. 592 - 604

Published: July 15, 2019

Language: Английский

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Data-Driven Strategies for Accelerated Materials Design

Accounts of Chemical Research, Journal Year: 2021, Volume and Issue: 54(4), P. 849 - 860

Published: Feb. 2, 2021

ConspectusThe ongoing revolution of the natural sciences by advent machine learning and artificial intelligence sparked significant interest in material science community recent years. The intrinsically high dimensionality space realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data tools developed increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls a clean energy transformation overhauling current technologies within only several years possible action available. Tackling this crisis requires development new at unprecedented pace scale. For example, organic photovoltaics have potential to replace existing silicon-based large extent open up fields application. In years, light-emitting diodes emerged as state-of-the-art technology digital screens portable devices enabling applications with flexible displays. Reticular frameworks allow atom-precise synthesis nanomaterials promise revolutionize field realize multifunctional nanoparticles from gas storage, separation, electrochemical storage nanomedicine. decade, advances all these been facilitated comprehensive application simulation property prediction, optimization, chemical exploration enabled considerable computing power algorithmic efficiency.In Account, we review most contributions our group thriving science. We start summary important classes has involved in, focusing on small molecules electronic crystalline materials. Specifically, highlight data-driven employed speed discovery derive design strategies. Subsequently, focus lies methodologies employed, elaborating high-throughput virtual screening, inverse molecular design, Bayesian supervised learning. discuss general ideas, their working principles, use cases examples successful implementations efforts. Furthermore, elaborate pitfalls remaining challenges methods. Finally, provide brief outlook foresee increasing adaptation implementation scale campaigns.

Language: Английский

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Nanoparticle synthesis assisted by machine learning

Nature Reviews Materials, Journal Year: 2021, Volume and Issue: 6(8), P. 701 - 716

Published: July 13, 2021

Language: Английский

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Machine Learning for Chemical Reactions

Chemical Reviews, Journal Year: 2021, Volume and Issue: 121(16), P. 10218 - 10239

Published: June 7, 2021

Machine learning (ML) techniques applied to chemical reactions have a long history. The present contribution discusses applications ranging from small molecule reaction dynamics computational platforms for planning. ML-based can be particularly relevant problems involving both computation and experiments. For one, Bayesian inference is powerful approach develop models consistent with knowledge Second, methods also used handle that are formally intractable using conventional approaches, such as exhaustive characterization of state-to-state information in reactive collisions. Finally, the explicit simulation networks they occur combustion has become possible machine-learned neural network potentials. This review provides an overview questions been addressed machine techniques, outlook challenges this diverse stimulating field. It concluded ML chemistry practiced conceived today potential transform way which field approaches reactions, research academic teaching.

Language: Английский

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The Role of Machine Learning in the Understanding and Design of Materials

Journal of the American Chemical Society, Journal Year: 2020, Volume and Issue: 142(48), P. 20273 - 20287

Published: Nov. 10, 2020

Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which have huge technological social impact. However, such requires a holistic perspective over full multistage process, involves exploring immense spaces, their properties, process engineering as well techno-economic assessment. The complexity all these options using conventional scientific seems intractable. Instead, tools from field machine learning potentially solve some our challenges on way design. Here we review chief advancements methods applications in design, followed by discussion main opportunities currently face together with future discovery.

Language: Английский

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