MDF-DTA: A Multi-Dimensional Fusion Approach for Drug-Target Binding Affinity Prediction.

Drug-target affinity (DTA) prediction is an important task in the early stages of drug discovery. Traditional biological approaches are time-consuming, effort-consuming, and resource-consuming due to the large size of genomic and chemical spaces. Computational approaches using machine learning have emerged to narrow down the drug candidate search space.

Identification of oral therapeutics using an AI platform against the virus responsible for COVID-19, SARS-CoV-2.

This study introduces a sophisticated computational pipeline, , designed for the discovery of antiviral drugs based on their interactions within the human protein network. There is a pressing need for cost-effective therapeutics for infectious diseases (e.g.

Break Down in Order To Build Up: Decomposing Small Molecules for Fragment-Based Drug Design with eMolFrag.

Constructing high-quality libraries of molecular building blocks is essential for successful fragment-based drug discovery. In this communication, we describe eMolFrag, a new open-source software to decompose organic compounds into nonredundant fragments retaining molecular connectivity information. Given a collection of molecules, eMolFrag generates a set of unique fragments comprising larger moieties, bricks, and smaller linkers connecting bricks.

A graph-based approach to construct target-focused libraries for virtual screening.

Background: Due to exorbitant costs of high-throughput screening, many drug discovery projects commonly employ inexpensive virtual screening to support experimental efforts. However, the vast majority of compounds in widely used screening libraries, such as the ZINC database, will have a very low probability to exhibit the desired bioactivity for a given protein. Although combinatorial chemistry methods can be used to augment existing compound libraries with novel drug-like compounds, the broad chemical space is often too large to be explored.