February 25, 2024

Scientists Develop GenAI Model to Accelerate Drug Design Process

In a groundbreaking development, scientists at Chapman University in Orange, California, have leveraged the power of generative artificial intelligence (GenAI) to design new drugs. Inspired by the success of platforms like ChatGPT and Midjourney, the scientists developed their own GenAI model called drugAI, which has the potential to revolutionize the drug design process.

Current methods of designing new synthetic drug compounds, also known as de novo drug design, can be time-consuming, labor-intensive, and costly. Seeking a more efficient approach, the team at Chapman University integrated two cutting-edge AI techniques in the fields of bioinformatics and cheminformatics: the Encoder-Decoder Transformer architecture and Reinforcement Learning via Monte Carlo Tree Search (RL-MCTS).

The drugAI platform was trained on a massive dataset of known chemicals, their binding properties to target proteins, and the rules and syntax of chemical structure and properties. By inputting a target protein sequence, the platform generates unique molecular structures that adhere to essential chemical and biological constraints and effectively bind to their targets.

Through an iterative refinement process, drugAI identifies 50-100 new molecules that have the potential to inhibit specific proteins associated with various diseases. The platform’s ability to generate previously unimagined drug candidates has been tested and validated, yielding promising results.

In assessing the quality of drugAI’s generated molecules, the researchers found that the candidate drugs had a validity rate of 100%—meaning none of them were present in the training set. Additionally, the drugAI-generated molecules exhibited strong binding affinities to their respective targets, comparable to those identified through traditional virtual screening approaches.

Comparisons with existing known drugs for specific diseases further demonstrated the capabilities of drugAI. In a study focused on COVID-19 proteins, drugAI generated a list of novel drugs targeting the same protein, which were found to have similar characteristics to natural products known to inhibit COVID-19 proteins. However, drugAI accomplished this in a much quicker and cost-effective manner.

One of the key advantages of drugAI is its flexible structure, which allows for the incorporation of new functions by future researchers. This versatility enhances the likelihood of identifying refined drug candidates with an even higher probability of becoming real drugs.

Overall, the development of drugAI represents a significant milestone in the field of drug design. By harnessing the power of GenAI, scientists can expedite the process of discovering viable drug candidates for a wide range of diseases, while also reducing costs. As researchers continue to explore its possibilities, the future looks promising for the integration of AI in drug development.

*Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it