CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots.

Authors

  • Leili Zhang
    IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598USA.
  • Giacomo Domeniconi
    IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA. gdomeniconi@ibm.com.
  • Chih-Chieh Yang
    IBM Thomas J. Watson Research Center, 1101 Kitchawan Rd, 10598, Yorktown Heights, NY, USA.
  • Seung-Gu Kang
    IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598USA.
  • Ruhong Zhou
    ZheJiang University, 688 Yuhangtang Road, Hangzhou, 310027, China.
  • Guojing Cong
    Oak Ridge national laboratory, 1 Bethel Valley Rd, 37830, Oak Ridge, TN, USA.