- BART: Bayesian additive regression trees
- Massively Multitask Networks for Drug Discovery
- Convolutional Networks on Graphs for Learning Molecular Fingerprints
- AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
- Gated Graph Sequence Neural Networks
- Protein-Ligand Scoring with Convolutional Neural Networks
- Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
- Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
- Neural Message Passing for Quantum Chemistry
- Self-Normalizing Neural Networks
- An Overview of Multi-Task Learning in Deep Neural Networks
- ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity?
- Learning Graph-Level Representation for Drug Discovery
- Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks
- SchNet − a deep learning architecture for molecules and materials
- DeepDTA: Deep Drug-Target Binding Affinity Prediction
- Junction Tree Variational Autoencoder for Molecular Graph Generation
- Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery
- Accelerating Prototype-Based Drug Discovery using Conditional Diversity Networks
- Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
- DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks
- CheMixNet: Mixed DNN Architectures for Predicting Chemical Properties using Multiple Molecular Representations
- KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
- GuacaMol: Benchmarking Models for De Novo Molecular Design
- Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
- Interpretable Deep Learning in Drug Discovery
- Strategies for Pre-training Graph Neural Networks
- All SMILES Variational Autoencoder
- Relational Dose-Response Modeling for Cancer Drug Studies
- Attention-based Multi-Input Deep Learning Architecture for Biological Activity Prediction: An Application in EGFR Inhibitors
- Hierarchical Graph-to-Graph Translation for Molecules
- ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations
- Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures
- Oblique Decision Trees from Derivatives of ReLU Networks
- Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
- SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery
- AMPL: A Data-Driven Modeling Pipeline for Drug Discovery
- Chemical-protein Interaction Extraction via Gaussian Probability Distribution and External Biomedical Knowledge
- Drug-Target Indication Prediction by Integrating End-to-End Learning and Fingerprints
- JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python
- Hierarchical Generation of Molecular Graphs using Structural Motifs
- Multi-Objective Molecule Generation using Interpretable Substructures
- Molecule Attention Transformer
- DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction
- Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
- DeepSIBA: Chemical Structure-based Inference of Biological Alterations
- DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction and Applications to Repurposing and Screening
- Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
- Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference
- Optimal Transport Graph Neural Networks
- We Should At Least Be Able To Design Molecules That Dock Well
- A Systematic Assessment of Deep Learning Models for Molecule Generation
- Protein Interface Prediction using Graph Convolutional Networks
- Sequential Experimental Design for Transductive Linear Bandits
- DeeplyTough: Learning Structural Comparison of Protein Binding Sites
- DEEPScreen: high performance drug–target interaction prediction with convolutional neural networks using 2-D structural compound representations
- Relaxed Conformal Prediction Cascades for Efficient Inference Over Many Labels
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