Molecular generative models such as REINVENT utilize RL to guide structure generation. In RL, compounds are first scored and the scores are then fed back to neural networks to update their parameters to increase the probability of structures with better score. In REINVENT, two types of multi-component scoring function were defined, in which individual components of the scoring function can be either combined as a weighted sum or as a weighted product as shown in Equation 1 and 2. Given a sequence x, the weight coefficient of individual score component 𝑝 𝑖 (𝑥) reflects its importance in the overall score S(x). For the weighted sum score (Eq. 1), the weight for each component should be a floating number in the [0,1] range and the sum of all weights should be 1.0. For the weighted product score, the overall score is defined as Eq. 2 and the weights can be any floating number.