In our conversation, we discuss Brandon's approach to problem-solving, the use of synthetic data, challenges facing the use of AI in drug development, why the diversity of both data and scientists is important, the three qualities required for innovation, and much more.Key Points From This Episode:We hear about Brandon’s unconventional background and professional career journey. Why he has a passion for combining AI and machine learning with biology.An outline of the Opal platform and how it is used for drug discovery.Brandon’s approach to innovating and improving various stages of pharmaceutical development.Whether or not he thinks his approach can be applied outside of pharmaceutical development.How data science is used in traditional companies and how this differs at Valo.What signs people should look out for to ensure they are at a data-driven organization. A brief discussion about the benefits of using non-traditional approaches. Ways in which Brandon sees synthetic data being used in the future.The biggest challenge currently limiting the use of synthetic data. A breakdown of the three competing qualities that are required to innovate.Reasons why Brandon thinks current algorithms and the underlying datasets need to be improved. Brandon shares his approach to ensuring fairness and rooting out bias in datasets.Another problem the industry faces with scientists: a lack of diversity.The value of re-weighting a training set.Innovations in AI and machine learning that keeps Brandon motivated and inspired.Tweetables:“Instead of improving the legacy, is there a way to really innovate and break things? And that’s the way we think about it here at Valo.” — @allg00d [0:08:46]“Here at Valo, if data scientists have good ideas, we let them run with them, you know? We let them commission experiments. That’s not generally the way that a traditional organization would work.” — @allg00d [0:11:31]“While you might be able to get synthetic data that represents the bulk, you are not going to get the resolution within those patients, within those subgroups, within the patient set.” — @allg00d [0:15:15]“We suffer right now from a lack of diversity of data, but then, on the other side, we also suffer as a field from lack of diversity in our scientists.” — @allg00d [0:19:42]Links Mentioned in Today’s Episode:Brandon AllgoodBrandon Allgood on LinkedInValoValo on LinkedInOpal platformDALI AllianceLogicaBrandon Allgood on TwitterRob Stevenson on LinkedInSama