Targeting non-alcoholic fatty liver disease: Design, X-ray co-crystal structure and synthesis of 'first-in-kind' inhibitors of serine/threonine kinase25.

We describe the synthesis of a series of 3-t-butyl 5-aminopyrazole p-substituted arylamides as inhibitors of serine-threonine25 (STK25), an enzyme implicated in the progression of non-alcoholic fatty liver disease (NAFLD). Appending a p-N-pyrrolidinosulphonamide group to the arylamide group led to a 'first-in kind' inhibitor with IC = 228 nM. A co-crystal structure with STK 25 revealed productive interactions which were also reproduced using molecular docking.

A compact review of molecular property prediction with graph neural networks.

As graph neural networks are becoming more and more powerful and useful in the field of drug discovery, many pharmaceutical companies are getting interested in utilizing these methods for their own in-house frameworks. This is especially compelling for tasks such as the prediction of molecular properties which is often one of the most crucial tasks in computer-aided drug discovery workflows. The immense hype surrounding these kinds of algorithms has led to the development of many different types of promising architectures and in this review we try to structure this highly dynamic field of AI-research by collecting and classifying 80 GNNs that have been used to predict more than 20 molecular properties using 48 different datasets.

Improved Lipophilicity and Aqueous Solubility Prediction with Composite Graph Neural Networks.

The accurate prediction of molecular properties, such as lipophilicity and aqueous solubility, are of great importance and pose challenges in several stages of the drug discovery pipeline. Machine learning methods, such as graph-based neural networks (GNNs), have shown exceptionally good performance in predicting these properties. In this work, we introduce a novel GNN architecture, called directed edge graph isomorphism network (D-GIN).

Artificial intelligence-enhanced drug design and development: Toward a computational precision medicine.

Artificial Intelligence (AI) relies upon a convergence of technologies with further synergies with life science technologies to capture the value of massive multi-modal data in the form of predictive models supporting decision-making. AI and machine learning (ML) enhance drug design and development by improving our understanding of disease heterogeneity, identifying dysregulated molecular pathways and therapeutic targets, designing and optimizing drug candidates, as well as evaluating in silico clinical efficacy. By providing an unprecedented level of knowledge on both patient specificities and drug candidate properties, AI is fostering the emergence of a computational precision medicine allowing the design of therapies or preventive measures tailored to the singularities of individual patients in terms of their physiology, disease features, and exposure to environmental risks.

New machine learning and physics-based scoring functions for drug discovery.

Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes.

Cheminformatics approach to exploring and modeling trait-associated metabolite profiles.

Developing predictive and transparent approaches to the analysis of metabolite profiles across patient cohorts is of critical importance for understanding the events that trigger or modulate traits of interest (e.g., disease progression, drug metabolism, chemical risk assessment).

Identifying individual risk rare variants using protein structure guided local tests (POINT).

Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set.

Confirmation of high-throughput screening data and novel mechanistic insights into VDR-xenobiotic interactions by orthogonal assays.

High throughput screening (HTS) programs have demonstrated that the Vitamin D receptor (VDR) is activated and/or antagonized by a wide range of structurally diverse chemicals. In this study, we examined the Tox21 qHTS data set generated against VDR for reproducibility and concordance and elucidated functional insights into VDR-xenobiotic interactions. Twenty-one potential VDR agonists and 19 VDR antagonists were identified from a subset of >400 compounds with putative VDR activity and examined for VDR functionality utilizing select orthogonal assays.

In silico Predicted Glucose-1-phosphate Uridylyltransferase (GalU) Inhibitors Block a Key Pathway Required for Listeria Virulence.

Peptidoglycan walls of gram positive bacteria are functionalized by glycopolymers called wall teichoic acid (WTA). In Listeria monocytogenes, multiple enzymes including the glucose-1-phosphate uridylyltransferase (GalU) were identified as mandatory for WTA galactosylation, so that the inhibition of GalU is associated with a significant attenuation of Listeria virulence. Herein, we report on a series of in silico predicted GalU inhibitors identified using structure-based virtual screening and experimentally validated to be effective in blocking the WTA galactosylation pathway in vitro.

Cheminformatics Analysis of Dynamic WNK-Inhibitor Interactions.

The With-No-Lysine (WNK) serine/threonine kinase family constitutes a unique and distinctive branch of the kinome. The four proteins of this family (WNK1/2/3/4) are involved in blood pressure regulation, body fluid, and electrolyte homeostasis. Herein, we modeled and analyzed the binding modes of all publicly-available small orthosteric and allosteric binders (including WNK463 and WNK467) experimentally tested towards any of the WNK family member.

Weaver's historic accessible collection of synthetic dyes: a cheminformatics analysis.

We present the Max Weaver Dye Library, a collection of ∼98 000 vials of custom-made and largely sparingly water-soluble dyes. Two years ago, the Eastman Chemical Company donated the library to North Carolina State University. This unique collection of chemicals, housed in the College of Textiles, also includes tens of thousands of fabric samples dyed using some of the library's compounds.

Privileged Substructures to Modulate Protein-Protein Interactions.

Given the difficulties to identify chemical probes that can modulate protein-protein interactions (PPIs), actors in the field have started to agree on the necessity to use PPI-tailored screening chemical collections. However, which type of scaffolds may promote the binding of compounds to PPI targets remains unclear. In this big data analysis, we have identified a list of privileged chemical substructures that are most often observed within inhibitors of PPIs.

Cheminformatics Modeling of Amine Solutions for Assessing their CO Absorption Properties.

As stricter regulations on CO emissions are adopted worldwide, identifying efficient chemical processes to capture and recycle CO is of critical importance for industry. The most common process known as amine scrubbing suffers from the lack of available amine solutions capable of capturing CO efficiently. Tertiary amines characterized by low heats of reaction are considered good candidates but their absorption properties can significantly differ from one analogue to another despite high structural similarity.

Imbalance in chemical space: How to facilitate the identification of protein-protein interaction inhibitors.

Protein-protein interactions (PPIs) play vital roles in life and provide new opportunities for therapeutic interventions. In this large data analysis, 3,300 inhibitors of PPIs (iPPIs) were compared to 17 reference datasets of collectively ~566,000 compounds (including natural compounds, existing drugs, active compounds on conventional targets, etc.) using a chemoinformatics approach.

Stabilization of protein-protein interaction complexes through small molecules.

Most of the small molecules that have been identified thus far to modulate protein-protein interactions (PPIs) are inhibitors. Another promising way to interfere with PPI-associated biological processes is to promote PPI stabilization. Even though PPI stabilizers are still scarce, stabilization of PPIs by small molecules is gaining momentum and offers new pharmacological options.

iPPI-DB: an online database of modulators of protein-protein interactions.

In order to boost the identification of low-molecular-weight drugs on protein-protein interactions (PPI), it is essential to properly collect and annotate experimental data about successful examples. This provides the scientific community with the necessary information to derive trends about privileged physicochemical properties and chemotypes that maximize the likelihood of promoting a given chemical probe to the most advanced stages of development. To this end we have developed iPPI-DB (freely accessible at http://www.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances.

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years.

Which three-dimensional characteristics make efficient inhibitors of protein-protein interactions?

The specific properties of protein-protein interactions (PPI) (flat, large and hydrophobic) make them harder to tackle with low-molecular-weight compounds. Learning from the properties of successful examples of PPI interface inhibitors (iPPI) at earlier stages of developments, has been pinpointed as a powerful strategy to circumvent this trend. To this end, we have computationally analyzed the bioactive conformations of iPPI and those of inhibitors of conventional targets (e.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology.

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow.

iPPI-DB: a manually curated and interactive database of small non-peptide inhibitors of protein-protein interactions.

The development of small molecule drugs targeting protein-protein interactions (PPI) represents a major challenge, in part owing to the misunderstanding of the PPI chemical space. To this end, we have manually collected the structures, the physicochemical and pharmacological profiles of 1650 PPI inhibitors across 13 families of PPI targets in a database named iPPI-DB. To access iPPI-DB, we propose a user-friendly web application (www.