Machine learning (ML) approaches are becoming ubiquitous in the broad fields of Life Sciences and Chemistry. The impact that ML approaches have had in these fields were unforeseen by the community at large and we are yet to understand the full impact that they will have in the mid to long term. Many of the subjects of study in Life Sciences and chemistry lie within the molecular scale, and specifically at this scale ML approaches have made incredible progress to solve long-standing problems in the field. It is clear that the novelty of the approach will fade, which will raise the standards of performance for the developed algorithms and the quest for interpretability of the “black boxes”.
In our track, we will attempt to gather some of the key players on the use of machine learning to deal with molecular entities and those that are looking into new challenges and problems that lie ahead. This will be the second year we are organizing the track which we believed raise a fair amount of interest within the audience last year.