It was also pointed out during the conference that regardless of the many statistics and metrics available to evaluate the performance of a predictive model, "true" validation requires prospective predictions and their experimental assessment. However, prospective predictions are not without Regardless of the speakers' diverse research environments and settings (Table ), it was clear from the meeting that the number of opportunities in ML in career development is increasing. This is happening in academia, in research institutes, and in large and small pharmaceutical companies. This outcome from the meeting was valuable for the students, particularly those wondering about their professional future in this area and having to decide about their next career steps . It was also valuable for students and early career investigators to become aware of the career paths of many speakers who have transitioned from different disciplines and have made significant scientific contributions in the exciting computer-aided drug design field. Several speakers with 20 to 30 or more years of experience, made the transition to computer-aided drug discovery from quantum mechanics, organic chemistry, biochemistry, computer engineering, medicine, and pharmacology. Their career paths are varied, and there is not a single straight path from one discipline to another. Research interests and opportunities evolve, and researchers adapt to the current needs, which can change.