Drug discovery with explainable artificial intelligence

Nature Machine Intelligence, Год журнала: 2020, Номер 2(10), С. 573 - 584

Опубликована: Окт. 13, 2020

Язык: Английский

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Machine learning for alloys

Nature Reviews Materials, Год журнала: 2021, Номер 6(8), С. 730 - 755

Опубликована: Июль 20, 2021

Язык: Английский

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Zwitterionic Biomaterials

Chemical Reviews, Год журнала: 2022, Номер 122(23), С. 17073 - 17154

Опубликована: Окт. 6, 2022

The term "zwitterionic polymers" refers to polymers that bear a pair of oppositely charged groups in their repeating units. When these are equally distributed at the molecular level, molecules exhibit an overall neutral charge with strong hydration effect via ionic solvation. constitutes foundation series exceptional properties zwitterionic materials, including resistance protein adsorption, lubrication interfaces, promotion stabilities, antifreezing solutions, etc. As result, materials have drawn great attention biomedical and engineering applications recent years. In this review, we give comprehensive panoramic overview covering fundamentals nonfouling behaviors, different types surfaces polymers, applications.

Язык: Английский

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State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis

Nature Communications, Год журнала: 2020, Номер 11(1)

Опубликована: Ноя. 4, 2020

We investigated the effect of different training scenarios on predicting (retro)synthesis chemical compounds using a text-like representation reactions (SMILES) and Natural Language Processing neural network Transformer architecture. showed that data augmentation, which is powerful method used in image processing, eliminated memorization by networks, improved their performance for prediction new sequences. This was observed when augmentation simultaneously input target simultaneously. The top-5 accuracy 84.8% largest fragment (thus identifying principal transformation classical retro-synthesis) USPTO-50k test dataset achieved combination SMILES beam search algorithm. same approach provided significantly better results direct from single-step USPTO-MIT set. Our model 90.6% top-1 96.1% its challenging mixed set 97% separated It also USPTO-full retrosynthesis both top-10 accuracies. appearance frequency most abundantly generated well correlated with outcome can be as measure quality reaction prediction.

Язык: Английский

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Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Chemical Reviews, Год журнала: 2022, Номер 122(16), С. 13478 - 13515

Опубликована: Июль 21, 2022

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, providing solutions environmental pollution. Improved processes for catalyst design better understanding electro/photocatalytic essential improving effectiveness. Recent advances in data science artificial intelligence have great potential accelerate electrocatalysis photocatalysis research, particularly rapid exploration large materials chemistry spaces through machine learning. Here comprehensive introduction to, critical review of, learning techniques used research provided. Sources electro/photocatalyst current approaches representing these by mathematical features described, most commonly methods summarized, quality utility models evaluated. Illustrations how applied novel discovery elucidate electrocatalytic or photocatalytic reaction mechanisms The offers guide scientists on selection research. application catalysis represents paradigm shift way advanced, next-generation catalysts will be designed synthesized.

Язык: Английский

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Advances in De Novo Drug Design: From Conventional to Machine Learning Methods

International Journal of Molecular Sciences, Год журнала: 2021, Номер 22(4), С. 1676 - 1676

Опубликована: Фев. 7, 2021

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of active site biological target or its known binders, respectively. Artificial intelligence, including ma-chine learning, an emerging field has positively impacted discovery process. Deep reinforcement learning subdivision machine combines artificial neural networks reinforcement-learning architectures. This method successfully been em-ployed to develop de approaches using variety recurrent networks, convolutional generative adversarial autoencoders. review article summarizes advances in conventional growth algorithms advanced machine-learning methodologies high-lights hot topics for further development.

Язык: Английский

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Machine learning the ropes: principles, applications and directions in synthetic chemistry

Chemical Society Reviews, Год журнала: 2020, Номер 49(17), С. 6154 - 6168

Опубликована: Янв. 1, 2020

Chemists go ML! This tutorial review provides easy access to the fundamentals of machine learning from a synthetic chemist's perspective. Its diverse applications for molecular design, synthesis planning, or reactivity prediction are summarized.

Язык: Английский

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Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

Journal of Chemical Information and Modeling, Год журнала: 2020, Номер 60(8), С. 3770 - 3780

Опубликована: Июль 23, 2020

Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and unanticipated imprecision wastes valuable time resources. The need UQ especially acute neural models, which are becoming increasingly standard yet challenging to interpret. While several approaches have been proposed in the literature, there no clear consensus on comparative performance these models. In this paper, we study question context regression tasks. We systematically evaluate methods five data sets using multiple complementary metrics. Our experiments show that none tested unequivocally superior all others, produces a reliable ranking errors across sets. believe results existing not sufficient common use cases further research needed, conclude with practical recommendation as techniques seem perform well relative others.

Язык: Английский

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Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Chemical Reviews, Год журнала: 2021, Номер 121(16), С. 9816 - 9872

Опубликована: Июль 7, 2021

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from chemistry methods. However, achieving this requires confluence coaction of expertise in computer science physical sciences. This Review is written for new experienced researchers working at the intersection both fields. We first provide concise tutorials machine methods, showing how involving can be achieved. follow with critical review noteworthy applications that demonstrate used together insightful (and useful) predictions molecular materials modeling, retrosyntheses, catalysis, drug design.

Язык: Английский

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A critical overview of computational approaches employed for COVID-19 drug discovery

Chemical Society Reviews, Год журнала: 2021, Номер 50(16), С. 9121 - 9151

Опубликована: Янв. 1, 2021

COVID-19 has resulted in huge numbers of infections and deaths worldwide brought the most severe disruptions to societies economies since Great Depression. Massive experimental computational research effort understand characterize disease rapidly develop diagnostics, vaccines, drugs emerged response this devastating pandemic more than 130 000 COVID-19-related papers have been published peer-reviewed journals or deposited preprint servers. Much focused on discovery novel drug candidates repurposing existing against COVID-19, many such projects either exclusively computer-aided studies. Herein, we provide an expert overview key methods their applications for small-molecule therapeutics that reported literature. We further outline that, after first year pandemic, it appears not produced rapid global solutions. However, several known used clinic cure patients, a few repurposed continue be considered clinical trials, along with candidates. posit truly impactful tools must deliver actionable, experimentally testable hypotheses enabling combinations, open science sharing results are critical accelerate development novel, much needed COVID-19.

Язык: Английский

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