arXiv:2008.09168
공식 코드 · Papers with Code
A Systematic Assessment of Deep Learning Models for Molecule Generation
원문 초록
In recent years the scientific community has devoted much effort in the development of deep learning models for the generation of new molecules with desirable properties (i.e. drugs). This has produced many proposals in literature. However, a systematic comparison among the different VAE methods is still missing. For this reason, we propose an extensive testbed for the evaluation of generative models for drug discovery, and we present the results obtained by many of the models proposed in literature.
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B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
Assay2Mol: large language model-based drug design using BioAssay context
PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning
Fast Bayesian Optimization of Function Networks with Partial Evaluations
Robust Molecular Property Prediction via Densifying Scarce Labeled Data
MTPNet: Multi-Grained Target Perception for Unified Activity Cliff Prediction
토론
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