oMeBench and oMeS provide the first large-scale expert-annotated benchmark and dynamic scoring method for assessing LLM performance on organic mechanism elucidation and multi-step reasoning.
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ChemBERTa- 2: Towards chemical foundation models
14 Pith papers cite this work. Polarity classification is still indexing.
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Chem-GMNet uses sphere-native embeddings, DualSKA attention, and SH-FFN layers to match or beat ChemBERTa-2 on MoleculeNet tasks with fewer parameters and sometimes no pretraining.
Screening of 52,000 bioRxiv preprints finds dual-use-adjacent content routinely present in open titles and abstracts, often exceeding risk thresholds.
Sparse autoencoders on MolFormer reveal position-tracking latents in early layers and atom-in-substructure plus pharmacologically relevant features in later layers, with non-canonical SMILES causing greater representation disruption than invalid ones.
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).
Benchmark across 78 endpoint-split entries finds classical ML winning 47.4% of best performances over pretrained models, GNNs, and LLMs, with performance depending on model-task-split fit rather than scale.
MIST models up to 10x larger than prior work, fine-tuned on over 400 structure-property tasks, match or exceed SOTA on benchmarks and demonstrate zero-shot olfactory perception mapping consistent with hyperbolic geometry.
HANNA is a thermodynamically consistent ML model for predicting excess Gibbs energy from molecular structures, trained on various binary mixture data and extended to multi-component mixtures using geometric projection.
SciCore-Mol augments LLMs with three integrated modules for molecular perception, latent diffusion generation, and reaction reasoning, claiming an 8B open model competes with or exceeds proprietary systems on chemical tasks.
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.
Bolek injects Morgan fingerprint embeddings into an instruction-tuned text model, then fine-tunes on molecular alignment and synthetic chain-of-thought tasks to improve performance and grounding on 15 TDC binary classification endpoints while generalizing to unseen tasks.
A systematic survey and benchmark of four deep learning paradigms for molecular property prediction that organizes the field, critiques current data practices, and outlines three future directions.
CVT archives with learned chemical embeddings improve median global hypervolume and multi-objective quality diversity in NLO molecular design compared to grid-based archives.
The authors compile an ordinal odor strength dataset for over 2,000 molecules from public sources and demonstrate supervised ML prediction of intensity categories, identifying molecular size, polarity, rings, and branching as key drivers via SHAP analysis.
citing papers explorer
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oMeBench: Towards Robust Benchmarking of LLMs in Organic Mechanism Elucidation and Reasoning
oMeBench and oMeS provide the first large-scale expert-annotated benchmark and dynamic scoring method for assessing LLM performance on organic mechanism elucidation and multi-step reasoning.
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Chem-GMNet: A Sphere-Native Geometric Transformer for Molecular Property Prediction
Chem-GMNet uses sphere-native embeddings, DualSKA attention, and SH-FFN layers to match or beat ChemBERTa-2 on MoleculeNet tasks with fewer parameters and sometimes no pretraining.
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The Biosecurity Blind Spot: Systematic Dual-use Detection in Open Science Infrastructure
Screening of 52,000 bioRxiv preprints finds dual-use-adjacent content routinely present in open titles and abstracts, often exceeding risk thresholds.
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What Does a Chemical Language Model Know About Molecules?
Sparse autoencoders on MolFormer reveal position-tracking latents in early layers and atom-in-substructure plus pharmacologically relevant features in later layers, with non-canonical SMILES causing greater representation disruption than invalid ones.
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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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Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction
Benchmark across 78 endpoint-split entries finds classical ML winning 47.4% of best performances over pretrained models, GNNs, and LLMs, with performance depending on model-task-split fit rather than scale.
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Foundation Models for Discovery and Exploration in Chemical Space
MIST models up to 10x larger than prior work, fine-tuned on over 400 structure-property tasks, match or exceed SOTA on benchmarks and demonstrate zero-shot olfactory perception mapping consistent with hyperbolic geometry.
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Thermodynamically consistent machine learning model for excess Gibbs energy
HANNA is a thermodynamically consistent ML model for predicting excess Gibbs energy from molecular structures, trained on various binary mixture data and extended to multi-component mixtures using geometric projection.
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SciCore-Mol: Augmenting Large Language Models with Pluggable Molecular Cognition Modules
SciCore-Mol augments LLMs with three integrated modules for molecular perception, latent diffusion generation, and reaction reasoning, claiming an 8B open model competes with or exceeds proprietary systems on chemical tasks.
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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.
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Bolek: A Multimodal Language Model for Molecular Reasoning
Bolek injects Morgan fingerprint embeddings into an instruction-tuned text model, then fine-tunes on molecular alignment and synthetic chain-of-thought tasks to improve performance and grounding on 15 TDC binary classification endpoints while generalizing to unseen tasks.
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A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Era
A systematic survey and benchmark of four deep learning paradigms for molecular property prediction that organizes the field, critiques current data practices, and outlines three future directions.
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CVT Archives and Chemical Embedding Measures for Multi-Objective Quality Diversity in Molecular Design
CVT archives with learned chemical embeddings improve median global hypervolume and multi-objective quality diversity in NLO molecular design compared to grid-based archives.
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Machine learning for smell: Ordinal odor strength prediction of molecular perfumery components
The authors compile an ordinal odor strength dataset for over 2,000 molecules from public sources and demonstrate supervised ML prediction of intensity categories, identifying molecular size, polarity, rings, and branching as key drivers via SHAP analysis.