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Publications
FusionRetro: Molecule Representation Fusion via Reaction Graph for Retrosynthetic Planning
Retrosynthetic planning is a fundamental problem in drug discovery and organic chemistry, which aims to find a complete multi-step syntheti… (see more)c route from a set of starting materials to the target molecule, determining crucial process flow in chemical production. Existing approaches combine single-step retrosynthesis models and search algorithms to find synthetic routes. However, these approaches generally consider the two pieces in a decoupled manner, taking only the product as the input to predict the reactants per planning step and largely ignoring the important context information from other intermediates along the synthetic route. In this work, we perform a series of experiments to identify the limitations of this decoupled view and propose a novel retrosynthesis framework that also exploits context information for retrosynthetic planning. We view synthetic routes as reaction graphs, and propose to incorporate the context by three principled steps: encode molecules into embeddings, aggregate information over routes, and readout to predict reactants. The whole framework can be efficiently optimized in an end-to-end fashion. Comprehensive experiments show that by fusing in context information over routes, our model sig-nificantly improves the performance of retrosyn-thetic planning over baselines that are not context-aware, especially for long synthetic routes.
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Learning from multiple related tasks by knowledge sharing and transfer has become increasingly relevant over the last two decades. In order … (see more)to successfully transfer information from one task to another, it is critical to understand the similarities and differences between the domains. In this paper, we introduce the notion of \emph{performance gap}, an intuitive and novel measure of the distance between learning tasks. Unlike existing measures which are used as tools to bound the difference of expected risks between tasks (e.g.,
Efficient computation of optimal transport distance between distributions is of growing importance in data science. Sinkhorn-based methods a… (see more)re currently the state-of-the-art for such computations, but require O(n2) computations. In addition, Sinkhorn-based methods commonly use an Euclidean ground distance between datapoints. However, with the prevalence of manifold structured scientific data, it is often desirable to consider geodesic ground distance. Here, we tackle both issues by proposing Geodesic Sinkhorn—based on diffusing a heat kernel on a manifold graph. Notably, Geodesic Sinkhorn requires only O(n log n) computation, as we approximate the heat kernel with Chebyshev polynomials based on the sparse graph Laplacian. We apply our method to the computation of barycenters of several distributions of high dimensional single cell data from patient samples undergoing chemotherapy. In particular, we define the barycentric distance as the distance between two such barycenters. Using this definition, we identify an optimal transport distance and path associated with the effect of treatment on cellular data.
2023-01-01
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) (published)
Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the p… (see more)ace of scientific discovery. While science has traditionally relied...
