Development of a method for evaluating drug-likeness and ease of synthesis using a data set in which compounds are assigned scores based on chemists' intuition.
The concept of drug-likeness, an important characteristic for any compound in a screening library, is nevertheless difficult to pin down. Based on our belief that this concept is implicit within the collective experience of working chemists, we devised a data set to capture an intuitive human understanding of both this characteristic and ease of synthesis, a second key characteristic. Five chemists assigned a pair of scores to each of 3980 diverse compounds, with the component scores of each pair corresponding to drug-likeness and ease of synthesis, respectively. Using this data set, we devised binary classifiers with an artificial neural network and a support vector machine. These models were found to efficiently eliminate compounds that are not drug-like and/or hard-to-synthesize derivatives, demonstrating the suitability of these models for use as compound acquisition filters.
Takaoka
Yuji
Y
Molecular Simulation Group, Research Center, Taisho Pharmaceutical Co., Ltd., 1-403 Yoshino-cho, Kita-ku, Saitama-shi, 331-9530 Saitama, Japan. yuji.takaoka@po.rd.taisho.co.jp
Endo
Yutaka
Y
Yamanobe
Susumu
S
Kakinuma
Hiroyuki
H
Okubo
Taketoshi
T
Shimazaki
Youichi
Y
Ota
Tomomi
T
Sumiya
Shigeyuki
S
Yoshikawa
Kensei
K
D020650:Combinatorial Chemistry Techniques / Q000379:methods*; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000138:chemical synthesis / Q000737:chemistry*; D013329:Structure-Activity Relationship
D004364:Pharmaceutical Preparations