On-device research index

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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8 results for “ADMET”

Step Change Improvement in ADMET Prediction with PotentialNet Deep Featurizationcs.LG

Deep featurization improves ADMET prediction accuracy.

problem Predicting ADMET properties to reduce clinical trial failures.

method Learning features from explicit molecular graphs using graph convolutions.

result Achieved unprecedented accuracy in ADMET property prediction.

ADMET deep featurization molecular prediction graph convolutions cross-validation

2019-03-28abs ↗pdf ↗

Modeling Industrial ADMET Data with Multitask Networksstat.ML

Deep learning methods such as multitask neural networks have recently been applied to ligand-based virtual screening and other drug discovery applications. Using a set of industrial ADMET datasets, we compare neural networks to standard baseline models and analyze multitask learning effects with both random cross-valid…

2016-06-28abs ↗pdf ↗

Un sous-système d'un système plat de dimension différentielle 2 est plat. A subsystem of a flat system of differential dimension 2 is flatmath.OC

Un sous-système de dimension différentielle au plus 2 d'une extension plate est plate. Si un tel système plat est stationnaire, il admet des sorties plates indépendantes du temps. A subsystem of a flat system of differential dimension at most 2 is flat. Furthermore, if such a flat system is stationary, we show that the…

2017-02-01abs ↗pdf ↗

Current Methods for Drug Property Prediction in the Real Worldq-bio.BM

Study links different drug property prediction methods and datasets.

problem Unclear which method is best for drug property prediction.

method Empirical study linking multiple datasets and methods.

result Best method depends on dataset, classical methods often outperform deep learning.

drug property prediction machine learning benchmark datasets uncertainty quantification QSAR

2023-07-25abs ↗pdf ↗

Gini in a Bottleneck: Sparse Molecular Representations for Graph Convolutional Neural Networkscs.LG

Sparse molecular representations improve interpretability in graph neural networks.

problem Difficulty in understanding which molecular graph aspects drive deep learning predictions.

method Constrain weights in a graph convolutional neural network using the Gini index to maximize representation inequality.

result The Gini-constrained approach does not degrade evaluation metrics and allows for interpretable representation combination.

graph convolutional neural networks sparse representations Gini index molecular interpretability deep learning explainability

2020-10-09abs ↗pdf ↗

Predicting drug properties with parameter-free machine learning: Pareto-Optimal Embedded Modeling (POEM)cs.LG

POEM predicts drug properties without tuning, outperforming other methods.

problem Predicting drug properties from molecular structures efficiently.

method POEM combines multiple molecular representations without hyperparameter tuning.

result POEM outperforms industry-standard methods across 17 tasks.

drug prediction machine learning POEM molecular properties non-parametric

2020-02-11abs ↗pdf ↗

DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixupcs.LG

DRFLM improves federated learning by handling data heterogeneity and noise.

problem Data heterogeneity and noise in federated learning.

method Distributionally robust optimization and mixup techniques.

result Enhanced global model prediction accuracy through robust optimization and local mixup.

federated learning distributionally robust optimization mixup data heterogeneity data noise

2022-04-16abs ↗pdf ↗

Can Tabular In-Context Learners Generalize to Biomolecular Property Prediction?cs.LG

Tabular in-context learners perform well on biomolecular tasks, but performance depends on the representation used.

problem Predicting biomolecular properties from limited labeled data.

method Evaluating tabular in-context learners on protein fitness regression and small-molecule classification tasks.

result Tabular in-context learners are competitive for protein fitness regression but not for small-molecule classification.

tabular in-context learning biomolecular property prediction protein engineering small-molecule design representation learning

2026-06-30abs ↗pdf ↗