Another way to assess the efficacy of ML-generated molecules would be through physics-based methods. For instance, binding-free-energy calculations which estimate how well a molecule binds to a specific part of a protein can be made quite accurate. Currently, they’re not used very often because of the computational cost, but this could be much less prohibitive as chips get faster (or ASICs for MD become easier to get) and so the models could explore chemical space without being restricted to only getting feedback from synthetically accessable molecules.
Another way to assess the efficacy of ML-generated molecules would be through physics-based methods. For instance, binding-free-energy calculations which estimate how well a molecule binds to a specific part of a protein can be made quite accurate. Currently, they’re not used very often because of the computational cost, but this could be much less prohibitive as chips get faster (or ASICs for MD become easier to get) and so the models could explore chemical space without being restricted to only getting feedback from synthetically accessable molecules.