Function determines form
New AI algorithm generates innovative substances on the basis of desired properties
Whether in medicine, battery research, or materials science, researchers everywhere are seeking innovative substances. In the process, they can often predict the desired chemical and physical properties in great detail, right down to atomic level. However, the range of all potential chemical compounds alone is so vast that it would take years to find the appropriate substance. An interdisciplinary research group at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at Technische Universität Berlin has now developed an algorithm which uses AI to implement inverse chemical design and thus generate targeted molecules based on their desired properties. The research group's publication titled "Inverse design of 3d molecular structures with conditional generative neural networks" has now been published in the renowned journal Nature Communications.
The search for suitable molecules for specific medical or industrial applications is an extremely complex and expensive process. "Hypothetically, there are an incredible number of possible structures. However, only a tiny fraction possesses the specific chemical or physical properties required for a particular application," explains Dr. Kristof Schütt, BIFOLD Junior Fellow at TU Berlin. A wealth of methods has been developed in recent years capable of predicting the chemical properties and energetic states of given substances using AI. But even using these efficient methods, the search for molecules with specific properties has proven difficult in practice, as it is still necessary to search through an overwhelming number of candidates.