Rickettsia Aglow: A Fluorescence Assay and Machine Learning Model to Identify Inhibitors of Intracellular Infection

The Experimentalist's Guide to Machine Learning for Small Molecule Design

Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML algorithms have accelerated scientific developments across a variety of research topics. The field of small molecule design is no exception, and an increasing number of researchers are applying ML techniques in their pursuit of discovering, generating, and optimizing small molecule compounds. The goal of this review is to provide simple, yet descriptive, explanations of some of the most commonly utilized ML algorithms in the field of small molecule design along with those that are highly applicable to an experimentally focused audience. The algorithms discussed here span across three ML paradigms: supervised learning, unsupervised learning, and ensemble methods. Examples from the published literature will be provided for each algorithm. Some common pitfalls of applying ML to biological and chemical data sets will also be explained, alongside a brief summary of a few more advanced paradigms, including reinforcement learning and semi-supervised learning.

Liquid chromatography-mass spectrometry-based protocol to measure drug accumulation in Mycobacterium tuberculosis and its host cell

The extent to which a drug accumulates in Mycobacterium tuberculosis (Mtb) and its host cell can affect treatment efficacy. We describe protocols measuring drug accumulation in Mtb, macrophages, and Mtb-infected macrophages. The method leverages drug extraction from the cellular lysate and drug-level quantification by liquid chromatography-mass spectrometry. The general methodology has broad applicability and can quantify drug accumulation in other cell types, while being extended to quantification of drug metabolites formed within the cell under study. For complete details on the use and execution of this protocol, please refer to Lavin et al. (2021).1.