AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor

PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces

题目：AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics文献来源：Nature Communications | (2022) 13:7238 1代码：https://github.com/MannLabs/alphapeptdeep简介：机器学习，特别是深度学习

AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor

在这里，我们回顾了药物发现背景下预测蛋白质-配体相互作用的计算方法，重点是使用人工智能(AI)的方法。我们首先简要介绍蛋白质（靶点），配体（例如药物）及其对非专家的相互作用。最后，我们调查和分析了用于预测蛋白质-配体结合位点，配体结合亲和力和结合姿势（构象）的机器学习(ML)方法，包括经典的ML算法和最近的深度学习方法。在探索了蛋白质-配体相互作用的这三个方面之间的相关性之后，有人提出应该统一研究

题目：AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics文献来源：Nature Communications | (2022) 13:7238 1代码：https://github.com/MannLabs/alphapeptdeep简介：机器学习，特别是深度学习

Organic reaction mechanism classification using machine learning

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives

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

On the Frustration to Predict Binding Affinities from Protein−LigandStructures with Deep Neural Networks