DDPD

To function as an effective drug, quantitative principles (e.g Lipinsiki’s Rule of Five) of molecular properties including the physicochemical and pharmacokinetics properties have to be obeyed so that the molecules can reach the drug target in sufficient concentration for enough time. Drug-likeness, a vital evaluation for compounds selection in early phase of drug discovery, is typically modeled by physicochemical and pharmacokinetic properties. Here, we establish The Digital Drug Property Database (DDPD), a structured digital database providing manually assembled and standardized drug property data of approved drugs. The overall aim is to provide high quality comprehensive resource of drug-likeness related properties including experimental physicochemical properties, pharmacokinetic/toxicokinetic properties and maximum dosages of approved drugs. Curated from literature and databases including DRUGBANK, T3DB, ATSDR and PDR, the assembled digital drug-likeness related drug property data is believed to be a useful resource for driving drug-likeness evaluation and prediction potentially with state-of-the-art Machine Learning based multivariate technologies and is hoped to become a hub for in silico modeling of drug-likeness to support drug discovery.

[05/01/2021] DDPD version 1.0 is fully ready for service.

Li Q, Ma SY, Zhang XL, Zhai ZY, Zhou L, Tao HD, Wang YC, Pan JB*. DDPD 1.0: a manually curated and standardized database of digital properties of approved drugs for drug-likeness evaluation and drug development. Database (Oxford). 2022 Feb 9; 2022: baab083.
DOI: 10.1093/database/baab083.
PMID: 35139189.