REVIEW 15 cited by
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
read the original abstract
Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their proficiency within specialized domains such as biomolecular studies remains limited. To address this challenge, we introduce Mol-Instructions, a comprehensive instruction dataset designed for the biomolecular domain. Mol-Instructions encompasses three key components: molecule-oriented instructions, protein-oriented instructions, and biomolecular text instructions. Each component aims to improve the understanding and prediction capabilities of LLMs concerning biomolecular features and behaviors. Through extensive instruction tuning experiments on LLMs, we demonstrate the effectiveness of Mol-Instructions in enhancing large models' performance in the intricate realm of biomolecular studies, thus fostering progress in the biomolecular research community. Mol-Instructions is publicly available for ongoing research and will undergo regular updates to enhance its applicability.
Forward citations
Cited by 15 Pith papers
-
Distribution-Aware Reward: Reinforcement Learning over Predictive Distributions for LLM Regression
Distribution-Aware Reward optimizes LLM regression by treating rollouts as empirical predictive distributions and rewarding marginal improvements in CRPS quality rather than point accuracy alone.
-
FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization
FORGE reformulates molecular optimization as context-aware fragment ranking and replacement using mined low-to-high edit pairs, outperforming larger language models and graph methods on standard benchmarks.
-
VibeProteinBench: An Evaluation Benchmark for Language-interfaced Vibe Protein Design
VibeProteinBench is a three-stage language-interfaced benchmark revealing that no current LLM performs strongly across recognition, engineering, and generation of proteins.
-
VibeProteinBench: An Evaluation Benchmark for Language-interfaced Vibe Protein Design
VibeProteinBench is a new benchmark evaluating LLMs on open-ended language-interfaced protein design across recognition, engineering, and generation, with no model showing strong performance in all areas.
-
TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology
A 52.6B-token multi-domain biology pretraining corpus with tool enrichment and new binding/localization instructions doubles a fixed base LLM's matched biology-eval score with little language forgetting.
-
Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data
A two-stage AI pipeline — spectral hypothesis generation followed by mass-constrained molecular refinement — reconstructs organic structures from multimodal spectra, with 93.8% top-1 accuracy on simulated QM9 data and...
-
TheBioCollection: Unified Pre-Training Scale LLM Corpus for Biology
TheBioCollection, a 52.6B-token unified biology corpus with tool-computed text and new instruction tasks, raises a fixed 16B LLM's score on its matched biology eval from 0.223 to 0.499 (2.24×).
-
Minibatch Selection for Language Models via Partition Matroid Constrained Gradient Matching
PartitionSel maximizes a validation-guided gradient-matching utility under partition-matroid per-domain budgets for cross-domain minibatch selection in LLM fine-tuning, showing gains over baselines on MetaMathQA and M...
-
SCI-PRM: A Tool Aware Process Reward Model for Scientific Reasoning Verification
Sci-PRM is a tool-aware process reward model trained on the SCIPRM70K dataset to provide fine-grained supervision for scientific reasoning and shown to boost foundation models via Best-of-N selection and RL.
-
Rethinking Molecular Text Representations for LLMs: An Empirical Study
Structured text representations like CML and MolJSON outperform SMILES variants on structural tasks while IUPAC dominates semantic tasks such as molecule retrieval across all tested LLMs.
-
MolDA: Molecular Understanding and Generation via Large Language Diffusion Model
MolDA is a multimodal molecular model that uses a discrete large language diffusion backbone plus a hybrid graph encoder to achieve better global coherence and validity than autoregressive approaches.
-
LatentChem: From Textual CoT to Latent Thinking in Chemical Reasoning
LatentChem reasons in continuous latent space for chemistry, achieving a 59.88% non-tie win rate over explicit CoT on ChemCoTBench with a 10.84x average reduction in reasoning overhead.
-
MolE-RAG: Molecular Structure-Enhanced Retrieval-Augmented Generation for Chemistry
MolE-RAG is a training-free RAG framework that augments LLMs with literature, molecular context, and structural analogs to improve performance on nine molecular property prediction tasks.
-
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.
-
Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances
Across the 68 papers it surveys, domain-specialized generative models usually outperform general-purpose LLMs on biological tasks, and agentic/conversational workflows are the least-covered topics.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.