Artificial intelligence (AI) has the potential to revolutionize the field of drug discovery. By analyzing datasets of biological and chemical information, AI algorithms can identify patterns and relationships that may not be apparent to human researchers. AI can be used to aid in the design of new drugs, by predicting the likely biological activity of potential compounds. This can save time and resources by allowing researchers to focus on the most promising candidates. In addition, AI can be used to study the complex interactions between drugs and biological systems, leading to a better understanding of how drugs work and how they can be improved
Did you guess that the text above was automatically generated with AI1) ? No? This showcases what deep learning can achieve. However, while the 'artificial intelligence revolution' is reshaping natural language processing, its potential is still untapped when it comes to drug discovery. Surely deep learning has accelerated tasks like synthesis prediction and molecule design, but we are still lacking methods to efficiently chart the vast 'chemical universe' and efficiently discover drugs for old and new diseases. These aspects provide an exciting opportunity to rethink current approaches for AI-driven drug discovery.
1) See openai.com/blog/chatgpt
to know more!
This PhD vacancy is part of an ERC-funded project, which aims to advance the potential of AI to discover new drugs, by providing innovative ways of representing - and learning from - molecular information with AI. The project will be fueled by methodological innovation and aimed to discover novel bioactive molecules, faster. Moreover, the novel approaches that you will develop will be applied prospectively in the wet lab thereby providing a unique opportunity to validate the AI predictions in a real-world setting.
Your tasks will include:
- Developing and implementing innovative algorithms to capture sophisticated chemical information with AI for drug discovery.
- Implementing cutting-edge deep learning approaches to efficiently learn from small data regimes.
- Collaborating and interacting with researchers in medicinal chemistry and chemical biology, to gain a deeper understanding of the underlying mechanisms, and for experimental validation.
- Communicating the results of your research through publications in scientific journals and presentations at conferences.
- Mentoring and supervising Master and Bachelor students who are working on related projects.
Your work will lie at the interface between AI, chemistry, and biology, and it will be propelled by creative and interdisciplinary thinking. You will become a member of the Molecular Machine Learning
team (led by Dr. F. Grisoni), whose mission is to augment human intelligence in drug discovery with innovation in AI. You will be embedded in the Chemical Biology group, the Dept. of Biomedical Engineering, and the Institute for Complex Molecular Systems, which are characterized by a highly interdisciplinary and collaborative approach to science.