Linear meta-learning surrogates trained across chemical objectives and auxiliary properties adapt rapidly to new multi-objective molecular searches and outperform baselines by 78% in Pareto performance on spin-crossover complexes.
MolE: a molecular foundation model for drug discovery
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Under Bemis-Murcko scaffold split on THP-1 DRUG-seq data, inverse-variance proxy ranks linear Morgan fingerprint regression highest while contest wMSE ranks deep fusion models highest, with fusion beating linear by -0.012 wMSE (p<10^-4).
MSAlign aligns frozen DreaMS and ChemBERTa models with MLPs and candidate-based contrastive learning to outperform prior methods on molecule retrieval from MS/MS spectra while quantifying distribution shift in data splits.
citing papers explorer
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Interpretable Meta-Learning for Multi-Objective Chemical Search
Linear meta-learning surrogates trained across chemical objectives and auxiliary properties adapt rapidly to new multi-objective molecular searches and outperform baselines by 78% in Pareto performance on spin-crossover complexes.
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The Metric Picks the Winner: Evaluation Choice Flips Model Rankings for Drug-Response Prediction in Unseen Chemistry
Under Bemis-Murcko scaffold split on THP-1 DRUG-seq data, inverse-variance proxy ranks linear Morgan fingerprint regression highest while contest wMSE ranks deep fusion models highest, with fusion beating linear by -0.012 wMSE (p<10^-4).
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MSAlign: Aligning Molecule and Mass Spectra Foundation Models for Metabolite Identification
MSAlign aligns frozen DreaMS and ChemBERTa models with MLPs and candidate-based contrastive learning to outperform prior methods on molecule retrieval from MS/MS spectra while quantifying distribution shift in data splits.