Deep learning models for compound design
Traditional drug development strategy is costly, tiresome, and labor-intensive. In the last decade, artificial intelligence (AI) technologies have shown promising results to overcome some of these limitations. However, these computational technologies still cannot efficiently generate novel drugs with expected properties for treating specific diseases. Here we will apply new generation of AI frameworks to design novel compounds with predefined properties. We will collect and analyze the publicly available data from peer-reviewed publications and in-house data to build the AI models. This research will help both the company to address some of its pressing needs and improve the interns’ skills for their future career development.