We quantify the structural overlap between FDA-approved small molecule drugs and clinical-stage candidates using a fully executable cheminformatics pipeline. Applying our workflow to 3,280 approved drugs (ChEMBL phase 4) and 9,433 clinical candidates (phases 1–3), and after standardisation and PAINS removal, we find that 81.
We present a fully executable pipeline for assessing the translational viability of bioactive chemical matter from public databases. Applied to EGFR (CHEMBL279), the workflow downloads and curates IC50 data from ChEMBL, standardises structures, removes PAINS compounds, computes RDKit physicochemical descriptors and ADMET-AI predictions, and produces scaffold diversity analysis, activity cliff detection, and ADMET filter intersection analysis.
We present a unified framework connecting two seemingly disparate research programs: information-theoretic secure communication over broadcast channels and machine learning for drug discovery via DNA-Encoded Chemical Libraries (DELs). Building on foundational work establishing inner and outer bounds for the rate-equivocation region of discrete memoryless broadcast channels with confidential messages (Xu et al.
Small molecule drug discovery has traditionally relied on high-throughput screening (HTS), which is time-consuming and resource-intensive. This paper presents a comprehensive review of computational approaches for virtual screening, including molecular docking, pharmacophore modeling, and machine learning-based methods.