cheminformatics

Review molecular ML, QSAR, docking, and retrosynthesis claims by evaluating molecule split strategies, descriptor reporting, and leakage risks. Separate methodological risks from wording risks, then provide precise revisions for methods, claims, and limitations.

Assess generation claims for validity, uniqueness, diversity, and synthesizability. Ensure claims are supported with data and methodology by checking descriptor reporting, model validation, and split strategies to detect data leakage and overfitting.

Detect data leakage by evaluating molecule split strategies including random, scaffold, temporal, and cluster splits. Review descriptor and fingerprint reporting to ensure methodological soundness and reproducibility in chemistry ML workflows.

Assess docking and screening targets by checking protonation states, controls, and scoring limitations. Evaluate docking pipeline claims to ensure supported methodology, detecting overfitting and unsupported assertions in drug discovery workflows.

Reproducibility risks in retrosynthesis and reaction prediction include unsupported claims, inadequate data handling, and insufficient validity checks. Audit these methodological issues by applying domain-specific criteria and clear revision guidance.

Yes, reviewing chemical generation claims requires domain-specific criteria focusing on reproducibility, data handling, and validity checks. Apply rigorous critique to detect leakage, overfitting, and unsupported assertions in molecular ML workflows.