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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.

168,695 papers · 148 categories

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6 results for “fragment-based”

FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproductionq-bio.BM

Improved RL model for fragment-based molecule generation.

problem Generating molecules with high docking scores.

method Thorough reproduction, scrutiny, and improvement of the FREED model.

result The improved model produces molecules with superior docking scores.

Reinforcement Learning Molecule Generation Docking Score Therapeutic Drugs Protein-Molecule Interactions

2024-01-18abs ↗pdf ↗

A Deep Generative Model for Fragment-Based Molecule Generationstat.ML

A new deep model generates molecules by fragments, improving validity and uniqueness.

problem Generating valid and unique molecules using deep learning.

method Develops a language model for molecular fragments, using frequency-based masking.

result Significantly outperforms other language model-based competitors in molecule generation.

molecule generation deep learning fragment-based drug design language model

2020-02-28abs ↗pdf ↗

Fragment Graphical Variational AutoEncoding for Screening Molecules with Small Dataphysics.data-an

In the majority of molecular optimization tasks, predictive machine learning (ML) models are limited due to the unavailability and cost of generating big experimental datasets on the specific task. To circumvent this limitation, ML models are trained on big theoretical datasets or experimental indicators of molecular s…

2019-10-21abs ↗pdf ↗

Symmetry-Aware GFlowNetsstat.ML

SA-GFN corrects biases in GFlowNets due to graph symmetries.

problem Systematic biases in state transition probability computations.

method Incorporates symmetry corrections into the learning process through reward scaling.

result Eliminates need for explicit state transition computations.

Generative Flow Networks Graph Symmetries Biases Reward Scaling Unbiased Sampling

2025-06-03abs ↗pdf ↗

Fragmentation in trader preferences among multiple markets: Market coexistence versus single market dominanceq-fin.TR

Model shows how multiple markets can coexist or fragment based on trader behavior.

problem Understanding market competition and coexistence among multiple trading venues.

method Stylized model of traders making repeated decisions at three markets, analyzed numerically and analytically.

result Parameters like memory length and choice intensity determine whether markets coexist or fragment.

trader preferences market competition market coexistence fragmentation memory length

2020-12-07abs ↗pdf ↗

Interpretable epistemic uncertainty decomposition in sequential generative models via polynomial chaos surrogatescs.LG

Polynomial chaos surrogates quantify epistemic uncertainty in AI-driven scientific models.

problem Uncertainty in reward estimates hinders interpretability in sequential generative models.

method Fit polynomial chaos expansions to trained models to propagate epistemic uncertainty and quantify sensitivity.

result Interpretable decomposition of reward components driving generative decisions.

epistemic uncertainty sequential generative models polynomial chaos Sobol sensitivity AI-driven discovery

2025-10-24abs ↗pdf ↗