Project

Research Area | Artificial Intelligence for Drug Discovery and Clinical Trials

In our published research findings we describe validation of novel machine learning architectures, with automated learning and predictions gleaned from the past experimental successes and failures of drugs, for designing faster, safer, and more efficacious clinical trials. This work has a significant impact on the ethical and regulatory decisions facing patients, the pharmaceutical industry, and the FDA and EMA. Phase 3 clinical outcome trials evaluating new drugs, therapies, and vaccines are among the most complex experiments performed in medicine. Around 50% of Phase 3 trials fail. The US Food and Drug Administration states that a common theme is the difficulty of predicting clinical results in a wide patient base, even with the backing of solid data. More importantly, the barriers to this cost healthcare industries, government, and academic research hospitals millions of dollars each year, as well as drive up costs, delay life-saving treatments to patients, and in some cases lead to adverse events. The crucial obstacle is a limited knowledge of the key parameters that need to be considered in order to test candidate… View full description

In our published research findings we describe validation of novel machine learning architectures, with automated learning and predictions gleaned from the past experimental successes and failures of drugs, for designing faster, safer, and more efficacious clinical trials. This work has a significant impact on the ethical and regulatory decisions facing patients, the pharmaceutical industry, and the FDA and EMA. Phase 3 clinical outcome trials evaluating new drugs, therapies, and vaccines are among the most complex experiments performed in medicine. Around 50% of Phase 3 trials fail. The US Food and Drug Administration states that a common theme is the difficulty of predicting clinical results in a wide patient base, even with the backing of solid data. More importantly, the barriers to this cost healthcare industries, government, and academic research hospitals millions of dollars each year, as well as drive up costs, delay life-saving treatments to patients, and in some cases lead to adverse events. The crucial obstacle is a limited knowledge of the key parameters that need to be considered in order to test candidate molecules, eliminate adverse events, and select optimal IC50.