Accelerating drug development process via digital transformation and open innovation

The Problem : Only 1/9000 success rate after 10~15 years

The Solution : Digital transformation of drug development process

Benchwork vs HITS digital solution


Innovation via fusion of physics and deep learning

Hit discovery


More about HITS technology


Hit Discovery

Predicting drug-target interaction using graph neural network

Journal of chemical information and modeling, 59 (9), 3981-3988

Jaechang Lim, Seongok Ryu, Kyubyong Park, Yo Joong Choe, Jiyeon Ham, and Woo Youn Kim
Hit Discovery

Attention- and gate-augmented graph convolutional network


Seongok Ryu, Jaechang Lim, Seung Hwan Hong, and Woo Youn Kim
Hit Discovery

Rational discovery of antimetastatic agents targeting the intrinsically disordered region of MBD2

Science Advances 5 (11), eaav9810

Min Young Kim, Insung Na, Ji Sook Kim, Seung Han Son, Sungwoo Choi, Seol Eui Lee, Ji-Hun Kim, Kiseok Jang, Gil Alterovitz, Yu Chen, Arjan van der Vaart, Hyung-Sik Won,Vladimir N. Uversky and Chul Geun Kim
Hit Discovery

A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification

Chemical Science, 2019, 10, 8438-8446

Seongok Ryu, Yongchan Kwon, and Woo Youn Kim

Scaffold-based molecular design with graph generative model

Chemical Science, 2020,11, 1153-1164

Jaechang Lim, Sang-Yeon Hwang, Seokhyun Moon, Seungsu Kim, and Woo Youn Kim

Molecular generative model with conditional variational autoencoder

Journal of Cheminformatics 10 (1), 31

Jaechang Lim, Seongok Ryu, Jin Woo Kim, and Woo Youn Kim

Molecular generative model based on adversarially regularized autoencoder

Journal of Chemical Information and Modeling, 2020, 60, 1, 29-36

Seung Hwan Hong, Seongok Ryu, Jaechang Lim, and Woo Youn Kim


Woo Youn Kim

CEO (cofounder)

  • B.S. in Chemistry and Physics, POSTECH
  • Ph.D. in Chemistry, POSTECH
  • Assistant and Associate professor, KAIST (2011~)

Jaechang Lim

Scientist (cofounder)

  • B.S. in Chemistry, KAIST
  • Ph.D. in Chemistry, KAIST
  • Development of deep learning techniques for drug discovery

Insung Na

Scientist (cofounder)

  • B.S. in Biology, Kyung Hee university
  • Staff. Microbiology analysis, QC, Celltrion
  • Ph.D. in Medical Sciences (concentration: molecular medicine), University of South Florida
  • Post doc, Boston Children’s Hospital / Harvard Medical School
  • Computational biology application for drug discovery




Contact Us

서울특별시 강남구 테헤란로 124 삼원타워 902호
Send an email to [email protected] for collaboration or job application