From Candidate Discovery to Validation... LG CNS Builds AI Drug Development Platform for Dong-A Socio Group

Supporting Key Research Processes with AI
Standardizing Dispersed Research Data

On August 12, LG CNS announced that it has completed the construction of an artificial intelligence (AI)-powered drug discovery platform for Dong-A Socio Group, in collaboration with Dong-A Socio Group’s IT affiliate, DAI (DAI Co., Ltd.).

The platform, which the two companies developed over approximately six months, integrates new drug research data and supports key research processes such as candidate molecule identification and validation through AI. In particular, as the AI learns by linking and training on both predicted results and actual experimental data, its predictive performance improves as research progresses.

Exterior view of LG CNS headquarters. LG CNS

By utilizing AI in the drug development process, candidate molecules with a higher likelihood of success can be quickly identified, and their efficacy and safety can be predicted in advance. This is expected to simultaneously reduce the typical drug development timeline of 10 to 15 years, as well as the associated costs and risk of failure.

Through the new platform, LG CNS has integrated and standardized drug research data previously dispersed across various sources, such as compound and genome information, experimental results, papers, and patents. Researchers can access all necessary data and use AI analysis and prediction features within a unified platform.

This AI platform supports each phase of the drug development process. At the stage of identifying disease causes, AI analyzes and visualizes gene information for individual cells and their spatial locations within tissues, helping researchers discover therapeutic targets more easily. During the candidate molecule design phase, generative AI directly designs new molecular structures that meet specific criteria, and in the validation stage, it simulates and predicts factors such as the binding possibility and activity stability between candidate molecules and their targets.

By comparing the AI’s predictions with actual experimental data, the models are retrained, progressively enhancing predictive performance as research data accumulates.

LG CNS has organically connected the entire process, from data collection and AI analysis to experimentation and verification, thereby improving the speed and quality of drug development while ensuring that accumulated research data can be leveraged as a valuable asset. In addition, recognizing the sensitive nature of drug research data, the company has established a management system that addresses regulatory and security requirements specific to the pharmaceutical industry.

Previously, LG CNS has demonstrated achievements in pharmaceutical and bio AI transformation (AX) projects, such as participating in the Ministry of Health and Welfare’s “K-AI New Drug Development Preclinical and Clinical Model Development Project (R&D)” and developing the “Agentic AI-based Annual Quality Evaluation Report Service” for Chong Kun Dang.

Jae-Seung Lee, Executive Director of Cloud Business at LG CNS, stated, “By leveraging differentiated AX technologies such as Agentic AI and our domain expertise in pharmaceuticals and biotechnology, we aim to drive innovation in AI-powered new drug research and development in Korea’s pharmaceutical and bio industries.” He added, “We will proactively support our clients to help them strengthen their competitiveness in new drug development.”

This content was produced with the assistance of AI translation services.