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Found 8 papers, 2 papers with code
DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction
In order to equip the model to generalize to conformations beyond the confines of crystal structures and to adapt to molecular docking and virtual screening tasks, we propose a multi-objective strategy, that is, the model outputs three scores for scoring and ranking, docking, and screening, and the training process optimizes these three objectives simultaneously.
Accelerating Discovery of Novel and Bioactive Ligands With Pharmacophore-Informed Generative Models
Deep generative models have gained significant advancements to accelerate drug discovery by generating bioactive chemicals against desired targets.
DiffBindFR: An SE(3) Equivariant Network for Flexible Protein-Ligand Docking
Furthermore, in the Apo and AlphaFold2 modeled structures, DiffBindFR demonstrates superior advantages in accurate ligand binding pose and protein binding conformation prediction, making it suitable for Apo and AlphaFold2 structure-based drug design.
Synthesis-driven design of 3D molecules for structure-based drug discovery using geometric transformers
Finding drug-like compounds with high bioactivity is essential for drug discovery, but the task is complicated by the high cost of chemical synthesis and validation.
MolMiner: You only look once for chemical structure recognition
Due to a backlog of decades and an increasing amount of these printed literature, there is a high demand for the translation of printed depictions into machine-readable formats, which is known as Optical Chemical Structure Recognition (OCSR).
Learning to design drug-like molecules in three-dimensional space using deep generative models
The architecture of L-Net is specifically optimized for drug-like molecules, and a set of metrics is assembled to comprehensively evaluate its performance.
Regulation of interferon production as a potential strategy for COVID-19 treatment
Regulating the upstream of the cytokines production could be a promising strategy to the treatment of COVID-19.
Deep Learning Based Regression and Multi-class Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction
The two external prediction accuracy of deepAOT-CR is 95. 0% and 94. 1%, while the R2 and MAE are 0. 861 and 0. 204 for test set I, respectively.
