Atomwise Inc. articles

Abstract

The application of machine learning techniques to cheminformatics data has accelerated tremendously in recent years.  In particular, deep learning architectures show considerable promise in being able to extract patterns and features from large datasets that aren’t readily obvious to human experts or apparent with hand-crafted features.  With this larger modeling c

Abstract

Virtual high-throughput screening of protein-ligand binding affinity is a valuable tool in computational drug design. Recently, structure-based machine-learning techniques have emerged as a powerful strategy for accurately predicting binding affinity. Simultaneously, SO(3)-equivariant neural networks can accurately predict physics-based properties of small molecules. The introduct

Abstract

After identifying and validating initial hits, drug discovery progresses through hit-to-lead and lead-optimization stages. Quantitatively predicting binding affinity accurately could greatly accelerate the drive to higher potency, opening a bigger window for addressing other drug endpoints. A global pKi prediction model across targets and ligands can bridge the gap between the ini

Abstract

With rapid advances in machine learning methods and the availability of vast amounts of chemical data, structure-based drug design is at the dawn of a golden age. The tremendous successes of deep learning methods in the fields of natural language processing, speech recognition and computer vision have set the expectation for these emerging technologies to successfully target undru

Abstract

Immune checkpoint inhibitors (ICIs) have obtained durable responses in many cancers, making it possible to foresee their potential in improving the health of cancer patients. However, immunotherapies are limited at the moment to a minority of patients and there is a need for a better understanding of the basic molecular mechanisms and functions of pivotal immune molecules. Immune checkpoint cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) plays an importa