In Latest Human Vs. Machine Match, Artificial Intelligence Wins By A Hair

Vikas Nanda has spent more than two decades studying the intricacies of proteins, the highly complex substances present in all living organisms. The Rutgers scientist has long contemplated how the unique patterns of amino acids that compose proteins determine whether they become anything from hemoglobin to collagen, as well as the subsequent, mysterious step of self-assembly where only certain proteins clump together to form even more complex substances. So, when scientists wanted to conduct an experiment pitting a human – one with a profound, intuitive understanding of protein design and self-assembly – against the predictive capabilities of an artificially intelligent computer program, Nanda, a researcher at the Center for Advanced Biotechnology and Medicine (CABM) at Rutgers, was one of those at the top of the list. Now, the results to see who – or what – could do a better job at predicting which protein sequences would combine most successfully are out. Nanda, along with researchers at Argonne National Laboratory in Illinois and colleagues from throughout the nation, reports in Nature Chemistry that the battle was close but decisive.