TY - JOUR
T1 - Learning Medicinal Chemistry Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Rules from Cross-Company Matched Molecular Pairs Analysis (MMPA)
AU - Kramer, Christian
AU - Ting, Attilla
AU - Zheng, Hao
AU - Hert, Jerome
AU - Schindler, Torsten
AU - Stahl, Martin
AU - Robb, Graeme
AU - Crawford, James J.
AU - Blaney, Jeff
AU - Montague, Shane
AU - Leach, Andrew G.
AU - Dossetter, Al G.
AU - Griffen, Ed J.
PY - 2018/4/26
Y1 - 2018/4/26
N2 - The first large scale analysis of in vitro absorption, distribution, metabolism, excretion, and toxicity (ADMET) data shared across multiple major pharma has been performed. Using advanced matched molecular pair analysis (MMPA), we combined data from three pharmaceutical companies and generated ADMET rules, avoiding the need to disclose the full chemical structures. On top of the very large exchange of knowledge, all companies involved synergistically gained approximately 20% more rules from the shared transformations. There is good quantitative agreement between the rules based on shared data compared to both individual companies’ rules and rules published in the literature. Known correlations between log D, solubility, in vitro clearance, and plasma protein binding also hold in transformation space, but there are also interesting exceptions. Data pools such as this allow focusing on particular functional groups and characterizing their ADMET profile. Finally the role of a corpus of robustly tested medicinal chemistry knowledge in the training of medicinal chemistry is discussed.
AB - The first large scale analysis of in vitro absorption, distribution, metabolism, excretion, and toxicity (ADMET) data shared across multiple major pharma has been performed. Using advanced matched molecular pair analysis (MMPA), we combined data from three pharmaceutical companies and generated ADMET rules, avoiding the need to disclose the full chemical structures. On top of the very large exchange of knowledge, all companies involved synergistically gained approximately 20% more rules from the shared transformations. There is good quantitative agreement between the rules based on shared data compared to both individual companies’ rules and rules published in the literature. Known correlations between log D, solubility, in vitro clearance, and plasma protein binding also hold in transformation space, but there are also interesting exceptions. Data pools such as this allow focusing on particular functional groups and characterizing their ADMET profile. Finally the role of a corpus of robustly tested medicinal chemistry knowledge in the training of medicinal chemistry is discussed.
U2 - 10.1021/acs.jmedchem.7b00935
DO - 10.1021/acs.jmedchem.7b00935
M3 - Article
SN - 0022-2623
VL - 61
SP - 3277
EP - 3292
JO - Journal of Medicinal Chemistry
JF - Journal of Medicinal Chemistry
IS - 8
ER -