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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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7 results for “pharmacophore”

Interpretable Deep Learning in Drug Discoverycs.LG

Interprets deep learning models in drug discovery to reveal hidden pharmacophores.

problem Neural networks in drug discovery are opaque; lack of interpretability limits their use.

method Identifies key components (pharmacophores/toxicophores) that influence model predictions.

result Extracted pharmacophores are consistent with literature findings and provide new insights.

deep learning drug discovery interpretability pharmacophore toxicophore

2019-03-07abs ↗pdf ↗

XIMP: Cross Graph Inter-Message Passing for Molecular Property Predictioncs.LG

XIMP improves molecular property prediction by integrating multiple graph representations.

problem Graph neural networks struggle in data-scarce regimes and fail to surpass traditional methods.

method Cross-graph inter-message passing with multiple graph abstractions.

result XIMP outperforms state-of-the-art baselines across diverse molecular property tasks.

molecular property prediction graph neural networks cross-graph inter-message passing multiple abstractions

2026-01-26abs ↗pdf ↗

Compositional Deep Probabilistic Models of DNA Encoded Librariesq-bio.QM

A deep probabilistic model analyzes DNA-encoded library data for efficient screening.

problem Complex data from DNA-encoded library experiments mask underlying signals.

method Compositional deep probabilistic model of DEL data, modeling latent reactions between synthons.

result DEL-Compose model demonstrates strong performance and valuable insights.

DNA-encoded libraries machine learning deep probabilistic models synthon latent reactions

2023-10-20abs ↗pdf ↗

Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filteringphysics.chem-ph

Deep filtering improves robustness of models from noisy, sparse data.

problem Challenges in understanding microscopic interactions from noisy, sparse, and biased data.

method Statistical approach based on deep filtering of nonlinear feature networks.

result Physicochemical models are more robust, transparent, and generalize better.

deep filtering nonlinear feature networks machine learning noisy data sparse data

2019-11-19abs ↗pdf ↗

SILVR: Guided Diffusion for Molecule Generationq-bio.BM

SILVR generates new molecules fitting protein binding sites.

problem Generating novel small molecule compounds for drug design.

method Selective Iterative Latent Variable Refinement (SILVR) for diffusion-based molecule generation.

result SILVR can generate new molecules similar in shape to original fragments without protein knowledge.

molecule generation drug design diffusion models protein binding fragment hits

2023-04-21abs ↗pdf ↗

Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistrycs.LG

Deep neural networks have achieved state of the art accuracy at classifying molecules with respect to whether they bind to specific protein targets. A key breakthrough would occur if these models could reveal the fragment pharmacophores that are causally involved in binding. Extracting chemical details of binding from …

2018-11-27abs ↗pdf ↗

Co-Diffusion: An Affinity-Aware Two-Stage Latent Diffusion Framework for Generalizable Drug-Target Affinity Predictionstat.ML

Co-Diffusion predicts drug-target affinity by learning latent manifolds and diffusion, improving generalization.

problem Cold-start regimes in drug-target affinity prediction due to label scarcity and domain shifts.

method Two-stage framework: latent manifold alignment and latent diffusion regularization.

result Significantly outperforms state-of-the-art baselines, especially in zero-shot generalization.

drug-target affinity latent diffusion generalization cold-start variational lower bound

2026-03-11abs ↗pdf ↗