It’s not exactly breaking news that biopharma companies are increasingly turning toward machine learning and artificial intelligence (AI) to improve drug development. What is big news is how resoundingly it might improve on traditional methods. This was recently demonstrated by the results of the Critical Assessment of Structure Prediction (CASP) contest hosted by the Protein Structure Prediction Center, sponsored by the U.S. National Institute of General Medical Sciences I(NIH/NIGMS).
CASP is a worldwide contest, or perhaps experiment is a better word, that has taken place every two years since 1994. Proteins have three-dimensional structures and those structures are an important element in how they function—it’s not uncommon, for example, for a mutation in the gene for a protein to result in the protein having a different shape, causing it to behave abnormally or not at all. And another fact to keep in mind is that protein shapes are dynamic, not necessarily static, transitioning between different states.
Every two years, the participants attempt to predict the 3-D shape of a specific human protein. Researchers focused on this type of research work to solve the problem using a variety of methods. Most recently, CASP wasn’t won by a biologist or biochemist, but by DeepMind, the AI laboratory owned by Google/Alphabet.
This article highlights the scope for developing therapeutics with new innovative, alternative approaches. The current trillion dollars pharmaceutical industry is in peril where the current drug development pipeline is slow, inefficient and incapable of being extended to multi-combination drug therapies as well as minimally focused on prevention. The complex modeling of diseases and biological functions has been limited because of the inability to integrate large scale molecular pathways.
Watch this Video to understand how recent advances provide breakthrough technology for doing scalable modeling of complex molecular systems to dramatically accelerate drug discovery and development.