1. MolSC: Leveraging Substituent Contributions to Enhance Fine-grained Molecular Understanding in LLMs1
The MolSC arXiv preprint, whose authors report EMNLP 2026 acceptance, trains molecular language models on how substituents change properties. Its benchmark separates scaffolds, substituents and molecules from training, exposing unreliable existing predictions and improvements after targeted training. This tests local chemical changes rather than whole-molecule labels alone. Reporting is limited to the original abstract.1
2. EVALUATING MOLECULAR DYNAMICS FRAMES FOR BINDING AFFINITY PREDICTION AND VIRTUAL SCREENING2
An unreviewed study tests molecular-dynamics frames as training data for binding-affinity scoring. Across five architectures, replacing crystal structures with simulated frames generally hurt prediction, while augmenting crystal data improved four architectures. This distinguishes useful structural perturbations from wholesale substitution of experimental poses. Our account is restricted to the deposited abstract and its reported computational benchmarks.2
3. Machine Learned Interatomic Forces as Inference-Time Physical Guidance for All-Atom Diffusion Peptide Design3
An unreviewed peptide-design study introduces interatomic-force guidance during diffusion-based generation. Across eight receptor systems, guidance reduced peptide–receptor clashes and improved estimated energetics, while retaining most generated sequences. This lets physical information influence generation before final relaxation. The evidence concerns computational geometry and energy estimates, not measured binding; our account is limited to the deposited abstract.3
4. ProtMutMap: Predicting Protein Binding Free Energy Changes Using Multiple-Mutation Networks4
The unreviewed ProtMutMap method combines multiple free-energy calculation paths to predict how several mutations change protein binding. Across 30 evaluation points from seven complexes, it achieved a 1.13 kcal/mol root-mean-square error against experimental values, outperforming the reported additive and stepwise comparisons. The result supports network-based treatment of interacting mutations; reporting is limited to the deposited abstract.4
5. Towards Accurate Prediction of Mutation-Induced Changes in Protein Structure5
An unreviewed analysis compares paired wild-type and single-mutant protein crystal structures with AlphaFold3 predictions. Observed structural changes concentrate near mutation sites, and prediction accuracy deteriorates as those changes grow. The study identifies a specific boundary for using predicted mutant structures in molecular investigation. This account is restricted to the original abstract, without a full-methods assessment.5
References
Park, Hyuntae; Kim, Sooyeon; Park, Jiwon; Lee, SangKeun. MolSC: Leveraging Substituent Contributions to Enhance Fine-grained Molecular Understanding in LLMs. arXiv; 2026; arXiv preprint v1; authors report acceptance to EMNLP 2026 Main Conference in arXiv comments; conference acceptance not independently verified. DOI: 10.48550/arXiv.2609.23073. Accessed 2026-09-23T16:20:22.264Z.
Source evidence and access
Abstract and submission history, arXiv:2609.23073; Cite-as DOI field (arXiv marks DataCite registration pending); Comments: Accepted to EMNLP 2026 Main Conference
Evidence paraphrase: The MolSC arXiv preprint, whose authors report EMNLP 2026 acceptance, trains molecular language models on how substituents change properties. Its benchmark separates scaffolds, substituents and molecules from training, exposing unreliable existing predictions and improvements after targeted training. This tests local chemical changes rather than whole-molecule labels alone. Reporting is limited to the original abstract.
Original arXiv abstract, submission history, comments and repository DOI inspected; arXiv labels DOI registration pending. Full text not reviewed.
Jakub Poziemski; Pawel Siedlecki. EVALUATING MOLECULAR DYNAMICS FRAMES FOR BINDING AFFINITY PREDICTION AND VIRTUAL SCREENING. ChemRxiv; 2026; Preprint v1; not peer reviewed. DOI: 10.26434/chemrxiv.15009259/v1. Accessed 2026-09-23T16:05:19.312Z.
Source evidence and access
Publisher-deposited abstract and first-online metadata, https://api.crossref.org/works/10.26434/chemrxiv.15009259/v1
Evidence paraphrase: An unreviewed study tests molecular-dynamics frames as training data for binding-affinity scoring. Across five architectures, replacing crystal structures with simulated frames generally hurt prediction, while augmenting crystal data improved four architectures. This distinguishes useful structural perturbations from wholesale substitution of experimental poses. Our account is restricted to the deposited abstract and its reported computational benchmarks.
Restricted to original publisher/repository-deposited abstract and metadata retrieved through Crossref; full text not inspected.
Biao Zeng; Youyi Song; Jinfeng Liu. Machine Learned Interatomic Forces as Inference-Time Physical Guidance for All-Atom Diffusion Peptide Design. ChemRxiv; 2026; Preprint v1; not peer reviewed. DOI: 10.26434/chemrxiv.15009322/v1. Accessed 2026-09-23T16:05:19.312Z.
Source evidence and access
Publisher-deposited abstract and first-online metadata, https://api.crossref.org/works/10.26434/chemrxiv.15009322/v1
Evidence paraphrase: An unreviewed peptide-design study introduces interatomic-force guidance during diffusion-based generation. Across eight receptor systems, guidance reduced peptide–receptor clashes and improved estimated energetics, while retaining most generated sequences. This lets physical information influence generation before final relaxation. The evidence concerns computational geometry and energy estimates, not measured binding; our account is limited to the deposited abstract.
Restricted to original publisher/repository-deposited abstract and metadata retrieved through Crossref; full text not inspected.
Kairi Furui; Masahito Ohue. ProtMutMap: Predicting Protein Binding Free Energy Changes Using Multiple-Mutation Networks. ChemRxiv; 2026; Preprint v1; not peer reviewed. DOI: 10.26434/chemrxiv.15009337/v1. Accessed 2026-09-23T16:05:19.312Z.
Source evidence and access
Publisher-deposited abstract and first-online metadata, https://api.crossref.org/works/10.26434/chemrxiv.15009337/v1
Evidence paraphrase: The unreviewed ProtMutMap method combines multiple free-energy calculation paths to predict how several mutations change protein binding. Across 30 evaluation points from seven complexes, it achieved a 1.13 kcal/mol root-mean-square error against experimental values, outperforming the reported additive and stepwise comparisons. The result supports network-based treatment of interacting mutations; reporting is limited to the deposited abstract.
Restricted to original publisher/repository-deposited abstract and metadata retrieved through Crossref; full text not inspected.
Liu, Zhuoyi; Calabrese, Alex; O'Hern, Corey S. Towards Accurate Prediction of Mutation-Induced Changes in Protein Structure. arXiv; 2026; Preprint v1; not peer reviewed. DOI: 10.48550/arXiv.2609.24842. Accessed 2026-09-23T16:20:22.264Z.
Source evidence and access
Abstract and submission history, arXiv:2609.24842; Cite-as DOI field (arXiv marks DataCite registration pending)
Evidence paraphrase: An unreviewed analysis compares paired wild-type and single-mutant protein crystal structures with AlphaFold3 predictions. Observed structural changes concentrate near mutation sites, and prediction accuracy deteriorates as those changes grow. The study identifies a specific boundary for using predicted mutant structures in molecular investigation. This account is restricted to the original abstract, without a full-methods assessment.
Original arXiv abstract, submission history, comments and repository DOI inspected; arXiv labels DOI registration pending. Full text not reviewed.
Publication record
Published 2026-09-23.
Sources, selection and claims were checked in an independent AI editorial review, followed by the AI editor's approval. This is not academic peer review.
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