Zero Shot Molecular Generation via Similarity Kernels

Gaussian, an approach known as denoising score matching [10-12]. In the context of molecule generation, the score is The combinatorial scaling of the available chemical closely related to atomic forces. Consider training data space with molecule size is one of the main challenges that comprise configurations sampled using molecular in the design of new molecules and materials. Generative dynamics or other methods from an underlying Boltzmann modelling aims to solve this by directly proposing distribution, x exp ( βU(x)) /Z. Here, x = structures with desirable properties, without exhaustively {r, z} is a set that represents a molecule, with r the enumerating and screening candidates. Recently, atomic positions and z the chemical elements, U(x) the diffusion-based models have achieved impressive results potential energy, β the inverse temperature, and Z the in molecular docking [1] and generation of linkers [2], partition function. In this case, when the elements z drug-like molecules [3, 4] and crystal structures [5, 6]. are fixed, the score of the data distribution s(x, 0) corresponds Diffusion models are trained to reverse a stochastic to the atomic force (defined as the negative gradient noising process, which gradually corrupts samples of of the potential energy) up to a multiplicative constant: training data until they are indistinguishable from samples drawn from an uninformative prior distribution, such as a standard Gaussian [7-9].