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Found 7 papers, 1 papers with code
Rethinking Specificity in SBDD: Leveraging Delta Score and Energy-Guided Diffusion
In the field of Structure-based Drug Design (SBDD), deep learning-based generative models have achieved outstanding performance in terms of docking score.
Contextual Molecule Representation Learning from Chemical Reaction Knowledge
REMO offers a novel solution to MRL by exploiting the underlying shared patterns in chemical reactions as \textit{context} for pre-training, which effectively infers meaningful representations of common chemistry knowledge.
Elastic Information Bottleneck
Comparing IB and DIB on these terms, we prove that DIB's SG bound is tighter than IB's while DIB's RD is larger than IB's.
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method
By aligning with physical principles, SliDe shows a 42\% improvement in the accuracy of estimated force fields compared to current state-of-the-art denoising methods, and thus outperforms traditional baselines on various molecular property prediction tasks.
ProFSA: Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment
Pocket representations play a vital role in various biomedical applications, such as druggability estimation, ligand affinity prediction, and de novo drug design.
Fractional Denoising for 3D Molecular Pre-training
Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks.
Multimodal Molecular Pretraining via Modality Blending
Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery.
