AI is proving itself to be a reliable method to analyse and understand complex problems, where the data generated through available experiments contains mainly nonlinear associations and interactions between variables, which are more difficult to analyse with traditional statistical methods. This capability of modern AI systems is being actively exploited in industry, where substantial investment is utilized on applying AI to the traditionally unsolved problems. A very good example is Alpha Fold, a recent neural network able to accurately predict protein folding. In this talk I am going to present other AI methods that we are developing in my laboratory to tackle similar problems with application to target discovery. One such family of approaches apples neural networks to genomics data, which will be the focus of this presentation.
