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| license: cc-by-nc-4.0 |
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| This dataset is constructed for supervised fine-tuning (SFT) of Y-Mol, a multimodal foundation model for drug discovery. It contains high-quality instruction–response pairs that span a wide range of drug-related tasks, designed to align the model with expert-like capabilities in molecular reasoning and decision-making. |
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| The dataset covers four key tasks: |
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| + Molecular Property Prediction |
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| Synthetic instruction–response pairs are generated using expert tools (e.g., ADMETlab, SwissADME) to simulate predictions of pharmacokinetic and physicochemical properties such as solubility, permeability, toxicity, and metabolic stability. |
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| + Molecule Generation |
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| Includes prompts and reference outputs for generating novel molecular structures conditioned on specific constraints such as bioactivity profiles, physicochemical rules, or scaffold constraints. |
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| + Drug–Target Interaction (DTI) Prediction |
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| Contains natural language prompts describing candidate drugs and targets, paired with binary or affinity-based interaction labels sourced or simulated from public databases and predictive models. |
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| + Drug–Drug Interaction (DDI) Prediction |
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| Captures interactions between drug pairs with both mechanistic and effect-level annotations, aiming to train the model to reason about combinatorial effects or contraindications. |
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| Each instruction–response pair is designed to be LLM-friendly and structured to encourage step-by-step reasoning, enabling Y-Mol to generalize across multiple biomedical and cheminformatics tasks. |
