Standards for Computational Methods in Drug Design and Discovery: Simplified Guidance for Authors and Reviewers

Computational methods are integral to modern drug discovery and development.However, manuscripts that apply these approaches superficially or without methodological rigor undermine scientific progress.Drug Design, Development and Therapy will therefore only consider computational studies that are transparent, reproducible, validated and biologically meaningful. 2D-QSAR studies will be rejected immediately. Predictions of bioactivities, ADMET, pharmacokinetics, or toxicity without experimental or external validation will be rejected. Black-box applications of computational servers or uncritical pipelines will not be accepted. Docking, virtual screening, molecular dynamics (MD), quantum mechanics/molecular mechanics (QM/MM), artificial intelligence and machine learning (AI/ML), and free energy approaches must be conducted and reported according to established best practices.Submissions not meeting these minimal requirements will not be sent for review. Part II: Detailed Guidelines QSAR and Molecular Modeling2D-QSAR studies are obsolete and unacceptable.3D-QSAR or more advanced models are permissible if applied to sufficiently large datasets, with rigorous alignment justification, proper cross-validation, and interpretable mechanistic insights. 1Molecular modeling may generate structural hypotheses but must not be overstated or presented as equivalent to experimental methods.Authors must highlight the model character of such work.Misrepresentation through selective omission or manipulative depictions will not be tolerated. Docking and Virtual ScreeningDocking studies are welcome when rationalizing structure-activity relationships or providing mechanistic support for experimental findings. 2,3Docking scores must not be reported as absolute binding energies.Binding site preparation, protonation states,