Exploring data-driven chemical SMILES tokenization approaches to identify key protein-ligand binding moieties.

Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular sequences are easily available, simple, and informative. The sequence-based models often segment molecular sequences into pieces called chemical words, analogous to the words that make up sentences in human languages, and then apply advanced natural language processing techniques for tasks such as de novo drug design, property prediction, and binding affinity prediction.

Machine-learning-based adverse drug event prediction from observational health data: A review.

Adverse drug events (ADEs) are responsible for a significant number of hospital admissions and fatalities. Machine learning models have been developed to assess the individual patient risk of having an ADE. In this article, we have reviewed studies addressing the prediction of ADEs in observational health data with machine learning.

Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition.

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health.

Exploiting pretrained biochemical language models for targeted drug design.

Motivation: The development of novel compounds targeting proteins of interest is one of the most important tasks in the pharmaceutical industry. Deep generative models have been applied to targeted molecular design and have shown promising results. Recently, target-specific molecule generation has been viewed as a translation between the protein language and the chemical language.

Carvacrol Enhances the Antimicrobial Potency of Berberine in Bacillus subtilis.

The essential oil carvacrol from oregano displays a wide range of biological activities among which is found the inhibition of efflux pumps. Thus, using carvacrol, the current work undertook the effort to potentiate the antimicrobial activity of berberine, a natural product with limited antimicrobial efficacy due to its efflux. Following the selection of concentrations for the combinatorial treatments, guided by checkerboard microtiter plate assay and growth experiments, ethidium bromide accumulation assay was used to find that 25 μg mL carvacrol displayed a weak efflux pump inhibitor character in Bacillus subtilis.

Labels in a haystack: Approaches beyond supervised learning in biomedical applications.

Recent advances in biomedical machine learning demonstrate great potential for data-driven techniques in health care and biomedical research. However, this potential has thus far been hampered by both the scarcity of annotated data in the biomedical domain and the diversity of the domain's subfields. While unsupervised learning is capable of finding unknown patterns in the data by design, supervised learning requires human annotation to achieve the desired performance through training.

ChemBoost: A Chemical Language Based Approach for Protein - Ligand Binding Affinity Prediction.

Identification of high affinity drug-target interactions is a major research question in drug discovery. Proteins are generally represented by their structures or sequences. However, structures are available only for a small subset of biomolecules and sequence similarity is not always correlated with functional similarity.

Exploring chemical space using natural language processing methodologies for drug discovery.

Text-based representations of chemicals and proteins can be thought of as unstructured languages codified by humans to describe domain-specific knowledge. Advances in natural language processing (NLP) methodologies in the processing of spoken languages accelerated the application of NLP to elucidate hidden knowledge in textual representations of these biochemical entities and then use it to construct models to predict molecular properties or to design novel molecules. This review outlines the impact made by these advances on drug discovery and aims to further the dialogue between medicinal chemists and computer scientists.

Superior inhibition of influenza virus hemagglutinin-mediated fusion by indole-substituted spirothiazolidinones.

The influenza virus hemagglutinin (HA) mediates membrane fusion after viral entry by endocytosis. The fusion process requires drastic low pH-induced HA refolding and is prevented by arbidol and tert-butylhydroquinone (TBHQ). We here report a class of superior inhibitors with indole-substituted spirothiazolidinone structure.

Synthesis and biological evaluation of indole-2-carbohydrazides and thiazolidinyl-indole-2-carboxamides as potent tubulin polymerization inhibitors.

A new series of N'-(substituted phenyl)-5-chloro/iodo-3-phenyl-1H-indole-2-carbohydrazide (5, 6) and N-[2-(substituted phenyl)-4-oxo-1,3-thiazolidin-3-yl]-5-iodo/chloro-3-phenyl-1H-indole-2-carboxamide (7, 8) derivatives were synthesized and evaluated for their anticancer properties. Compounds 5a and 6b, selected as prototypes by the National Cancer Institute for screening against the full panel of 60 human tumor cell lines at a minimum of five concentrations at 10-fold dilutions, demonstrated remarkable antiproliferative activity against leukemia, non-small cell lung cancer, colon cancer, central nervous system (CNS) cancer, melanoma, ovarian cancer, renal cancer, and breast cancer (MCF-7) cell lines with GI values < 0.4 μM.

DeepDTA: deep drug-target binding affinity prediction.

Motivation: The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge.

Membrane Active Peptides and Their Biophysical Characterization.

In the last 20 years, an increasing number of studies have been reported on membrane active peptides. These peptides exert their biological activity by interacting with the cell membrane, either to disrupt it and lead to cell lysis or to translocate through it to deliver cargos into the cell and reach their target. Membrane active peptides are attractive alternatives to currently used pharmaceuticals and the number of antimicrobial peptides (AMPs) and peptides designed for drug and gene delivery in the drug pipeline is increasing.

Targeting a hidden site on class A beta-lactamases.

Increasing resistance against available orthosteric beta-lactamase inhibitors necessitates the search for novel and powerful inhibitor molecules. In this respect, allosteric inhibitors serve as attractive alternatives. Here, we examine the structural basis of inhibition in a hidden, druggable pocket in TEM-1 beta-lactamase.

A novel methodology on distributed representations of proteins using their interacting ligands.

Motivation: The effective representation of proteins is a crucial task that directly affects the performance of many bioinformatics problems. Related proteins usually bind to similar ligands. Chemical characteristics of ligands are known to capture the functional and mechanistic properties of proteins suggesting that a ligand-based approach can be utilized in protein representation.

pVEC hydrophobic N-terminus is critical for antibacterial activity.

Cell-penetrating peptides (CPPs) are commonly defined by their shared ability to be internalized into eukaryotic cells, without inducing permanent membrane damage, and to improve cargo delivery. Many CPPs also possess antimicrobial action strong enough to selectively lyse microbes in infected mammalian cultures. pVEC, a CPP derived from cadherin, is able to translocate into mammalian cells, and it is also antimicrobial.

Investigation of novel pharmacological chaperones for Gaucher Disease.

Beta-Glucocerebrosidase (GBA) is a lysosomal protein that is responsible for the hydrolysis of glycosylceramide into glucose and ceramide. Mutations in GBA lead to the accumulation of glycosylceramide in the lysosome causing an enlargement of the spleen and the liver and skeletal deformations. This disease is called Gaucher Disease.

Sphingosine kinase 1 (SK1) allosteric inhibitors that target the dimerization site.

The sphingosine kinase 1 (SK1)/sphingosine-1-phosphate (S1P) signaling pathway is a crucial target for numerous human diseases from cancer to cardiovascular diseases. However, available SK1 inhibitors that target the active site suffer from poor potency, selectivity and pharmacokinetic properties. The selectivity issue of the kinases, which share a highly-conserved ATP-pocket, can be overcome by targeting the less-conserved allosteric sites.

The effect of a beta-lactamase inhibitor peptide on bacterial membrane structure and integrity: a comparative study.

Co-administration of beta-lactam antibiotics and beta-lactamase inhibitors has been a favored treatment strategy against beta-lactamase-mediated bacterial antibiotic resistance, but the emergence of beta-lactamases resistant to current inhibitors necessitates the discovery of novel non-beta-lactam inhibitors. Peptides derived from the Ala46-Tyr51 region of the beta-lactamase inhibitor protein are considered as potent inhibitors of beta-lactamase; unfortunately, peptide delivery into the cell limits their potential. The properties of cell-penetrating peptides could guide the design of beta-lactamase inhibitory peptides.

Response of Escherichia coli to Prolonged Berberine Exposure.

Berberine is a plant-derived alkaloid possessing antimicrobial activity; unfortunately, its efflux through multidrug resistance pumps reduces its efficacy. Cellular life span of Escherichia coli is generally shorter with prolonged berberine exposure; nevertheless, about 30% of the cells still remain robust following this treatment. To elucidate its mechanism of action and to identify proteins that could be involved in development of antimicrobial resistance, protein profiles of E.

In Silico Identification of Novel Orthosteric Inhibitors of Sphingosine Kinase 1 (SK1).

Background: Sphingosine kinase 1 (SK1) overexpression and elevated sphingosine-1-phosphate (S1P) levels have been correlated with many disease states from cancer to inflammatory diseases to diabetes. Even though SK1 inhibitors are of consideberable interest as effective chemotherapeutic agents, poor potency, lack of selectivity and poor pharmacokinetic properties have been major problems in the first generation SK1 inhibitors.

Objective: There is an urgent need for the discovery of novel in vivo, stable selective SK1 inhibitors with improved potency.

A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction.

Background: Molecular structures can be represented as strings of special characters using SMILES. Since each molecule is represented as a string, the similarity between compounds can be computed using SMILES-based string similarity functions. Most previous studies on drug-target interaction prediction use 2D-based compound similarity kernels such as SIMCOMP.