A curated list of awesome lists on Machine Learning for Drug Discovery
- Machine learning in preclinical drug discovery
- Plant Metabolite Databases: From Herbal Medicines to Modern Drug Discovery
- Artificial intelligence for natural product drug discovery
- Machine learning in preclinical drug discovery
- The Jungle of Generative Drug Discovery: Traps, Treasures, and Ways Out
- Advancing Molecular Machine (Learned) Representations with Stereoelectronics-Infused Molecular Graphs
- Bioactivity descriptors for uncharacterized chemical compounds
- MiniMol : A Parameter-Efficient Foundation Model for Molecular Learning
- Application of the mol2vec Technology to Large-size Data Visualization and Analysis
- SIMG: Chemical Representation and Interaction Discovery with Stereoelectronics-Infused Molecular Graphs
- Essay list about Molecular Generation or Drug Discovery
- minimol
- A deep graph model for the signed interaction prediction in biological network
- Drug-target interaction prediction by integrating heterogeneous information with mutual attention network
- A Bayesian machine learning approach for drug target identification using diverse data types
- Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study
- Knowledge Graph Convolutional Network with Heuristic Search for Drug Repositioning
- KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction
- Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability
- Drug target prediction through deep learning functional representation of gene signatures
- A Bayesian machine learning approach for drug target identification using diverse data types
- Multi-Objective Latent Space Optimization of Generative Molecular Design Models
- ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
- Integration of Genetic Algorithms and Deep Learning for the Generation and Bioactivity Prediction of Novel Tyrosine Kinase Inhibitors
- A pharmacophore-guided deep learning approach for bioactive molecular generation
- Molecular Graph Generation by Decomposition and Reassembling
- Multi-task Neural Networks for QSAR Predictions
- Development and Evaluation of Conformal Prediction Methods for QSAR
- QComp: A QSAR-Based Data Completion Framework for Drug Discovery
- Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks
- Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks
- Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction
- Exploring QSAR models for activity-cliff prediction
- Analyzing Learned Molecular Representations for Property Prediction
- Predicting Drug-Induced Liver Injury Using Convolutional Neural Network and Molecular Fingerprint-Embedded Features
- Transformers for molecular property prediction: Lessons learned from the past five years
- Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling !! Dự kiến: Sắp xếp theo từng năm.
- Drug Side Effect Prediction with Deep Learning Molecular Embedding in a Graph-of-Graphs Domain
- Predicting Side Effect of Drug Molecules using Recurrent Neural Networks
- A Deep Learning Approach to the Prediction of Drug Side–Effects on Molecular Graphs
- Cheminformatics in Natural Product-Based Drug Discovery