Improved Protein–Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference
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A major challenge in contemporary electronic structure theory involves the development of methods to describe in a balanced manner the contribution of correlation effects to energy differences. This challenge can be even greater for multicomponent systems containing more than one type of quantum particle. In the present work, we describe a flexible code for carrying out self-consistent field and configuration interaction (CI) calculations on multicomponent systems and use it to generate trial wave functions for use in diffusion Monte Carlo (DMC) calculations of the positron affinity of Be, Be 2 , Be 4 , Mg, CS 2 , and benzene. The resulting positron affinities (PAs) are in good agreement with the best values from the literature.
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