A Validation-First Workflow for Structure-Based Drug Design

Structure-based drug design (SBDD) uses three-dimensional information about a biological target to guide the identification and optimisation of small molecules. Molecular docking, virtual screening, molecular dynamics, free-energy calculations, and artificial-intelligence methods can help prioritise compounds for experimental testing. However, a plausible docking pose or favourable score is not proof of binding or therapeutic activity. Outcomes can be affected by protein structure quality, protonation, ligand preparation, receptor flexibility, scoring-function limitations, and the chemical space represented in a screening library. This narrative review presents a validation-first workflow: define a biologically justified target; assess the target structure and binding site; prepare proteins and ligands transparently; validate docking using reference ligands and appropriate controls; combine structural interpretation with complementary ranking; assess absorption, distribution, metabolism, excretion and toxicity (ADMET) risks; and confirm prioritised candidates experimentally. Recent AI-enabled methods are considered as tools for hypothesis generation rather than substitutes for medicinal chemistry judgement or laboratory evidence. The review also discusses reproducibility, uncertainty, and transparent reporting. The central recommendation is to connect every computational stage to a clear decision gate and to distinguish predictions from observations. This framework is intended to support student and early-stage research projects; it is not a report of new experimental data or a claim that any compound has been discovered.