Drug discovery is the task of applying machine learning to discover new candidate drugs.
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We present fusion models to benefit from different feature representations of two neural network models to improve the binding affinity prediction.
Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models.
Finally, we present a novel inference approach, where the trained models are used to estimate the signaling pathways affected by a compound perturbation in a specific cell line, using only its chemical structure as input.
Recently, with the increasing amount of affinity data available and the success of deep representation learning models on various domains, deep learning techniques have been applied to DTA prediction.
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs.
Designing a single neural network architecture that performs competitively across a range of molecule property prediction tasks remains largely an open challenge, and its solution may unlock a widespread use of deep learning in the drug discovery industry.
Indeed, as we demonstrate, their performance degrades significantly for larger molecules.
We propose a message passing scheme analogous to belief propagation, which uses the directional information by transforming messages based on the angle between them.
SOTA for Drug Discovery on QM9
Finally, since all of the simulation code is written in Python, researchers can have unprecedented flexibility in setting up experiments without having to edit any low-level C++ or CUDA code.
Computer-Aided Drug Discovery research has proven to be a promising direction in drug discovery.