Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects

Machine Learning with Applications, Год журнала: 2024, Номер 17, С. 100576 - 100576

Опубликована: Июль 24, 2024

Язык: Английский

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Machine learning-guided strategies for reaction conditions design and optimization

Beilstein Journal of Organic Chemistry, Год журнала: 2024, Номер 20, С. 2476 - 2492

Опубликована: Окт. 4, 2024

This review surveys the recent advances and challenges in predicting optimizing reaction conditions using machine learning techniques. The paper emphasizes importance of acquiring processing large diverse datasets chemical reactions, use both global local models to guide design synthetic processes. Global exploit information from comprehensive databases suggest general for new while fine-tune specific parameters a given family improve yield selectivity. also identifies current limitations opportunities this field, such as data quality availability, integration high-throughput experimentation. demonstrates how combination engineering, science, ML algorithms can enhance efficiency effectiveness design, enable novel discoveries chemistry.

Язык: Английский

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ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials

Digital Discovery, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

ArcaNN is a comprehensive framework that employs concurrent learning to generate training datasets for reactive MLIPs in the condensed phase.

Язык: Английский

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Recent advances from computer-aided drug design to artificial intelligence drug design

RSC Medicinal Chemistry, Год журнала: 2024, Номер 15(12), С. 3978 - 4000

Опубликована: Янв. 1, 2024

CADD and AIDD contribute to the drug discovery.

Язык: Английский

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Multi‐modal Homogeneous Chemical Reaction Performance Prediction with Graph and Chemical Language Information

Chinese Journal of Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Фев. 20, 2025

Comprehensive Summary Accurate prediction for chemical reaction performance offers optimal direction synthetic development. To this end, we present a novel multi‐modal model called MMHRP‐GCL to achieve the of homogeneous yield, enantioselectivity, and activation energy by fusing information from text graph modalities, requiring only 8 simple descriptors Reaction SMILES obtained without high‐cost DFT computation, capable managing reactions involving fluctuating number molecules. Experimental results on 4 datasets show that outperforms at least 7 generalized SOTA methods. Ablation study confirms critical roles complementation as well significance modality alignment atomic features in prediction. Albeit there is still room improvement interpretation relationships, has remarkable ability identify important atoms. A statistically interpretable feature importance test challenging dataset further demonstrates utility potential model. As high‐accuracy, low‐cost, interpretable, general model, provides valuable guidance design forward predictors catalytic reactions.

Язык: Английский

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Integrating a multitask graph neural network with DFT calculations for site-selectivity prediction of arenes and mechanistic knowledge generation

Nature Synthesis, Год журнала: 2025, Номер unknown

Опубликована: Апрель 7, 2025

Язык: Английский

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A meta-learning approach for selectivity prediction in asymmetric catalysis

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Апрель 15, 2025

Abstract Transition metal-catalyzed asymmetric reactions are of high contemporary importance in organic synthesis. Recently, machine learning (ML) has shown promise accelerating the development newer catalytic protocols. However, need for large amount experimental data can present a bottleneck implementing ML models. Here, we propose meta-learning workflow that harness literature-derived to extract shared reaction features and requires only few examples predict outcome new reactions. Prototypical networks used as method enantioselectivity hydrogenation olefins. This model consistently provides significant performance improvement over other popular methods such random forests graph neural networks. The our meta-model is analyzed with varying sizes training demonstrate its utility even limited data. A good on an out-of-sample test set further indicates general applicability approach. We believe this work will provide leap forward identifying promising early phases when minimal available.

Язык: Английский

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Evolutionary features for task-specific machine-learning applications

Chem, Год журнала: 2024, Номер 10(6), С. 1623 - 1626

Опубликована: Июнь 1, 2024

Язык: Английский

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Anti‐inflammatory effects of natural products from vitamin C‐rich fruits

Food Frontiers, Год журнала: 2024, Номер unknown

Опубликована: Сен. 4, 2024

Abstract Inflammation is a crucial target for therapeutic interventions in many life‐threatening diseases, which sustains ongoing interest the field of inflammation biology. Plant‐derived natural products, rich phytochemicals, have been used as healing agents several diseases since antiquity. These compounds exhibit antioxidant, anti‐inflammatory, and immunomodulatory properties, well gut microbiota modulation. They hold substantial potential promising candidates development novel strategies management inflammation‐associated diseases. This study presents comprehensive overview benefits given from administrating products (e.g., phenols, terpenes, flavonoids, saccharides), with particular emphasis on vitamin C‐rich fruits based high content bioactive anti‐inflammatory properties. Apart acts significant role modulating activation inflammatory reaction. Deviations its composition associated various Furthermore, advancements machine learning contribute to enhancing clinical outcomes disease treatment. Therefore, this work provided some valuable insights elaborating fruits, probiotics agents, utilization computer‐aided drug design techniques.

Язык: Английский

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Machine learning-assisted retrosynthesis planning: current status and future prospects

Chinese Journal of Chemical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 1, 2024

Язык: Английский

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