AI-generated molecular fingerprints help identify existing compounds with therapeutic potential
Health care researchers have long sought to harness the power of computing to design molecules that bind to specific receptors and can be used as targeted treatments for diseases. Even with the increasing sophistication of artificial intelligence, however, this technological promise has yet to be realized.
Researchers are utilizing AI-generated molecular fingerprints to identify existing compounds with therapeutic potential, as outlined in a study published in Science Advances. Junmei Wang, a professor of pharmaceutical sciences, developed a framework called Full-Spectrum Generative Lead Discovery (FSGLD) that leverages generative AI to screen for compounds with therapeutic value.
The study demonstrates that FSGLD can identify novel compounds that bind to specific receptors, such as G protein-coupled receptors (GPCRs) and protein kinases, without the need for synthesizing new molecules. This approach offers a practical and reusable method for compound discovery, as it utilizes simpler molecular fingerprints rather than generating entirely new molecules.
Wang's lab is further exploring two complementary approaches, ADSeqGAN and DRUG-FF, which generate new molecular structures from text descriptions and use receptor shapes to guide compound design, respectively.
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