Cancer therapeutics development effectively boils down to a massive-scale optimization problem over a highly multi-dimensional surface. The status quo in the in

Cancer therapeutics development effectively boils down to a massive-scale optimization problem over a highly multi-dimensional surface. The status quo in the industry is to say "let's find a sufficiently good local optimum by having humans largely-manually try to make transformations to known molecules" in this space. But with software, we can address this issue way more systematically. Here are some questions we can ask (and answer with software) to drive this process more rationally: * How might you represent the molecules that define this space? (lots of graph theory, linear algebra, vector math) * How would you model their biochemical properties? (w00t supervised machine learning, but also physics simulations) * Once you build a model for a properties, how can you deploy that model at billion-scale to start searching this space? (containerization, distributed systems, k8s) * How do you iteratively improve your compounds (generative machine learning, evolutionary optimization, lots of search/RL methods) * Once you choose a compound you like and get some experimental data about it, how does that new ground truth value change your model? (data warehousing, data pipelining) * If that data is valuable, how do you keep it safe? (network security, ACLs, virtual private cloud configurations, VPNs, etc.) * How can we visualize what our data and predictions look like? (visualization, endless types of front-end development) So yes, if you want to attack cancer through a data-driven process, there are TONS of ways for any kind of software engineer to join a team that drives that forward. (Disclaimer, I’m the founder/CTO of a YC-backed startup developing therapeutics in-house. We’re hiring people for all of the above roles to work on curing cancer. Check us out at https://reverielabs.com/careers https://reverielabs.com/careers)