MolE: a foundation model for molecular graphs using disentangled attention.

Models that accurately predict properties based on chemical structure are valuable tools in the chemical sciences. However, for many properties, public and private training sets are typically small, making it difficult for models to generalize well outside of the training data. Recently, this lack of generalization has been mitigated by using self-supervised pretraining on large unlabeled datasets, followed by finetuning on smaller, labeled datasets.

Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds.

We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries.

Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets.

Pharmaceutical or phytopharmaceutical molecules rely on the interaction with one or more specific molecular targets to induce their anticipated biological responses. Nonetheless, these compounds are also prone to interact with many other non-intended biological targets, also known as off-targets. Unfortunately, off-target identification is difficult and expensive.

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence.

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data.

Lysine harvesting is an antioxidant strategy and triggers underground polyamine metabolism.

Both single and multicellular organisms depend on anti-stress mechanisms that enable them to deal with sudden changes in the environment, including exposure to heat and oxidants. Central to the stress response are dynamic changes in metabolism, such as the transition from the glycolysis to the pentose phosphate pathway-a conserved first-line response to oxidative insults. Here we report a second metabolic adaptation that protects microbial cells in stress situations.

Towards understanding polyol additive effects on the pH shift-induced aggregation of a monoclonal antibody using high throughput screening and quantitative structure-activity modeling.

pH shift-induced aggregation is frequently observed in downstream processing of monoclonal antibodies and has been shown to depend on solvent composition. To quantify the stabilizing effect of polyol additives against aggregation, we determined aggregation rate constants in the presence of a set of 14 compounds. Rate constants were then correlated with molecular descriptors in a quantitative structure activity relationship (QSAR) approach.

Activity Landscape Plotter: A Web-Based Application for the Analysis of Structure-Activity Relationships.

Activity landscape modeling is a powerful method for the quantitative analysis of structure-activity relationships. This cheminformatics area is in continuous growth, and several quantitative and visual approaches are constantly being developed. However, these approaches often fall into disuse due to their limited access.

Diversity selection, screening and quantitative structure-activity relationships of osmolyte-like additive effects on the thermal stability of a monoclonal antibody.

Solvents used for therapeutic proteins in downstream processing and in formulations often contain stabilizing additives that inhibit denaturation and aggregation. Such additives are mostly selected based on their positive effect on thermal stability of the protein, and are often derived from naturally occuring osmolytes. To better understand the structural basis underlying the effect of additives, we selected a diverse library of compounds comprising 79 compounds of the polyol, amino acid and methylamine chemical classes and determined the effect of each compound on thermal stability of a monoclonal antibody as a function of compound concentration.

The many roles of molecular complexity in drug discovery.

Molecular complexity is becoming a crucial concept in drug discovery. It has been associated with target selectivity, success in progressing into clinical development and compound safety, among other factors. Multiple metrics have been developed to quantify molecular complexity and explore complexity-property relationships.

Synthesis, Screening and in silico Simulations of Anti-Parasitic Propamidine/Benzimidazole Derivatives.

Background: We designed hybrid molecules between propamidine and benzimidazole in order to retain the antiprotozoal action, but decreasing the toxic effect of the molecule.

Objective: Design and prepare 12 hybrids for testing their antiparasitic effect over three protozoa: Giardia intestinalis, Trichomonas vaginalis and Leishmania mexicana, as well as conduct several in silico simulations such as toxicological profile, molecular docking and molecular dynamics in order to understand their potential mode of action.

Methods: Hybrids 1-3, 6-9 and 12 were obtained using a chemical pathway previously reported.

Chemoinformatic expedition of the chemical space of fungal products.

Aim: Fungi are valuable resources for bioactive secondary metabolites. However, the chemical space of fungal secondary metabolites has been studied only on a limited basis. Herein, we report a comprehensive chemoinformatic analysis of a unique set of 207 fungal metabolites isolated and characterized in a USA National Cancer Institute funded drug discovery project.

Statistical correlation of nonconservative substitutions of HIV gp41 variable amino acid residues with the R5X4 HIV-1 phenotype.

Background: The interaction of the envelope glycoprotein of HIV-1 (gp120/gp41) with coreceptor molecules has important implications for specific cellular targeting and pathogenesis. Experimental and theoretical evidences have shown a role for gp41 in coreceptor tropism, although there is no consensus about the positions involved. Here we analyze the association of physicochemical properties of gp41 amino acid residues with viral tropism (X4, R5, and R5X4) using a large set of HIV-1 sequences.

ARWAR: A network approach for predicting Adverse Drug Reactions.

Predicting novel drug side-effects, or Adverse Drug Reactions (ADRs), plays an important role in the drug discovery process. Existing methods consider mainly the chemical and biological characteristics of each drug individually, thereby neglecting information hidden in the relationships among drugs. Complementary to the existing individual methods, in this paper, we propose a novel network approach for ADR prediction that is called Augmented Random-WAlk with Restarts (ARWAR).

Design, synthesis and evaluation of semi-synthetic triazole-containing caffeic acid analogues as 5-lipoxygenase inhibitors.

In this work the synthesis, structure-activity relationship (SAR) and biological evaluation of a novel series of triazole-containing 5-lipoxygenase (5-LO) inhibitors are described. The use of structure-guided drug design techniques provided compounds that demonstrated excellent 5-LO inhibition with IC50 of 0.2 and 3.

Activity cliffs and activity cliff generators based on chemotype-related activity landscapes.

Activity cliffs have large impact in drug discovery; therefore, their detection and quantification are of major importance. This work introduces the metric activity cliff enrichment factor and expands the previously reported activity cliff generator concept by adding chemotype information to representations of the activity landscape. To exemplify these concepts, three molecular databases with multiple biological activities were characterized.

Activity and property landscape modeling is at the interface of chemoinformatics and medicinal chemistry.

Property landscape modeling (PLM) methods are at the interface of experimental sciences and computational chemistry. PLM are becoming a common strategy to describe systematically structure-property relationships of datasets. Thus far, PLM have been used mainly in medicinal chemistry and drug discovery.

Analyzing multitarget activity landscapes using protein-ligand interaction fingerprints: interaction cliffs.

Activity landscape modeling is mostly a descriptive technique that allows rationalizing continuous and discontinuous SARs. Nevertheless, the interpretation of some landscape features, especially of activity cliffs, is not straightforward. As the nature of activity cliffs depends on the ligand and the target, information regarding both should be included in the analysis.

Discovery and development of DNA methyltransferase inhibitors using in silico approaches.

Multiple strategies have evolved during the past few years to advance epigenetic compounds targeting DNA methyltransferases (DNMTs). Significant progress has been made in HTS, lead optimization and determination of 3D structures of DNMTs. In light of the emerging concept of epi-informatics, computational approaches are employed to accelerate the development of DNMT inhibitors helping to screen chemical databases, mine the DNMT-relevant chemical space, uncover SAR and design focused libraries.

The interplay between molecular modeling and chemoinformatics to characterize protein-ligand and protein-protein interactions landscapes for drug discovery.

Protein-ligand and protein-protein interactions play a fundamental role in drug discovery. A number of computational approaches have been developed to characterize and use the knowledge of such interactions that can lead to drug candidates and eventually compounds in the clinic. With the increasing structural information of protein-ligand and protein-protein complexes, the combination of molecular modeling and chemoinformatics approaches are often required for the efficient analysis of a large number of such complexes.

Modelling ligand selectivity of serine proteases using integrative proteochemometric approaches improves model performance and allows the multi-target dependent interpretation of features.

Serine proteases, implicated in important physiological functions, have a high intra-family similarity, which leads to unwanted off-target effects of inhibitors with insufficient selectivity. However, the availability of sequence and structure data has now made it possible to develop approaches to design pharmacological agents that can discriminate successfully between their related binding sites. In this study, we have quantified the relationship between 12,625 distinct protease inhibitors and their bioactivity against 67 targets of the serine protease family (20,213 data points) in an integrative manner, using proteochemometric modelling (PCM).

Rationalization of activity cliffs of a sulfonamide inhibitor of DNA methyltransferases with induced-fit docking.

Inhibitors of human DNA methyltransferases (DNMT) are of increasing interest to develop novel epi-drugs for the treatment of cancer and other diseases. As the number of compounds with reported DNMT inhibition is increasing, molecular docking is shedding light to elucidate their mechanism of action and further interpret structure-activity relationships. Herein, we present a structure-based rationalization of the activity of SW155246, a distinct sulfonamide compound recently reported as an inhibitor of human DNMT1 obtained from high-throughput screening.

Toward drug repurposing in epigenetics: olsalazine as a hypomethylating compound active in a cellular context.

DNA hypomethylating drugs that act on DNA methyltransferase (DNMT) isoforms are promising anticancer agents. By using a well-characterized live-cell system to measure DNA methylation revisions (imprints), we characterize olsalazine, an approved anti-inflammatory drug, as a novel DNA hypomethylating agent. The cell-based screen used in this work is highly tractable, internally controlled, and well-suited for a drug repurposing strategy in epigenetics.

Molecular basis for benzimidazole resistance from a novel β-tubulin binding site model.

Benzimidazole-2-carbamate derivatives (BzCs) are the most commonly used antiparasitic drugs for the treatment of protozoan and helminthic infections. BzCs inhibit the microtubule polymerization mechanism through binding selectively to the β-tubulin subunit in which mutations have been identified that lead to drug resistance. Currently, the lack of crystallographic structures of β-tubulin in parasites has limited the study of the binding site and the analysis of the resistance to BzCs.