Machine Learning Models to Predict Protein-Protein Interaction Inhibitors.

Protein-protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available.

Progress on open chemoinformatic tools for expanding and exploring the chemical space.

The concept of chemical space is a cornerstone in chemoinformatics, and it has broad conceptual and practical applicability in many areas of chemistry, including drug design and discovery. One of the most considerable impacts is in the study of structure-property relationships where the property can be a biological activity or any other characteristic of interest to a particular chemistry discipline. The chemical space is highly dependent on the molecular representation that is also a cornerstone concept in computational chemistry. Herein, we discuss the recent progress on chemoinformatic tools developed to expand and characterize the chemical space of compound data sets using different types of molecular representations, generate visual representations of such spaces, and explore structure-property relationships in the context of chemical spaces. We emphasize the development of methods and freely available tools focusing on drug discovery applications. We also comment on the general advantages and shortcomings of using freely available and easy-to-use tools and discuss the value of using such open resources for research, education, and scientific dissemination.

Latin American databases of natural products: biodiversity and drug discovery against SARS-CoV-2.

In this study, we evaluated 3444 Latin American natural products using cheminformatic tools. We also characterized 196 compounds for the first time from the flora of El Salvador that were compared with the databases of secondary metabolites from Brazil, Mexico, and Panama, and 42 969 compounds (natural, semi-synthetic, synthetic) from different regions of the world. The overall analysis was performed using drug-likeness properties, molecular fingerprints of different designs, two parameters similarity, molecular scaffolds, and molecular complexity metrics. It was found that, in general, Salvadoran natural products have a large diversity based on fingerprints. Simultaneously, those belonging to Mexico and Panama present the greatest diversity of scaffolds compared to the other databases. This study provided evidence of the high structural complexity that Latin America's natural products have as a benchmark. The COVID-19 pandemic has had a negative effect on a global level. Thus, in the search for substances that may influence the coronavirus life cycle, the secondary metabolites from El Salvador and Panama were evaluated by docking against the endoribonuclease NSP-15, an enzyme involved in the SARS CoV-2 viral replication. We propose in this study three natural products as potential inhibitors of NSP-15.

In Silico ADME/Tox Profiling of Natural Products: A Focus on BIOFACQUIM.

Natural products continue to be major sources of bioactive compounds and drug candidates not only because of their unique chemical structures but also because of their overall favorable metabolism and pharmacokinetic properties. The number of publicly accessible natural product databases has increased significantly in the past few years. However, the systematic ADME/Tox profile has been reported on a limited basis. For instance, BIOFACQUIM was recently published as a public database of natural products from Mexico, a country with a rich source of biomolecules. However, its ADME/Tox profile has not been reported. Herein, we discuss the results of an in-depth in silico ADME/Tox profile of natural products in BIOFACQUIM and other large public collections of natural products. It was concluded that the absorption and distribution profiles of compounds in BIOFACQUIM are similar to those of approved drugs, while the metabolism profile is comparable to that in the other natural product databases. The excretion profile of compounds in BIOFACQUIM is different from that of the approved drugs, but their predicted toxicity profile is comparable. This work further contributes to the deeper characterization of natural product collections as major sources of bioactive compounds with therapeutic potential.

D-Peptide Builder: A Web Service to Enumerate, Analyze, and Visualize the Chemical Space of Combinatorial Peptide Libraries.

Peptide-based drug discovery is re-gaining attention in drug discovery. Similarly, combinatorial chemistry continues to be a useful technique for the rapid exploration of chemical space. A current challenge, however, is the enumeration of combinatorial peptide libraries using freely accessible tools. To facilitate the swift enumeration of combinatorial peptide libraries, we introduce herein D-Peptide Builder. In the current version, the user can build up to pentapeptides, linear or cyclic, using the natural pool of 20 amino acids. The user can use non- and/or N-methylated amino acids. The server also enables the rapid visualization of the chemical space of the newly enumerated peptides in comparison with other libraries relevant to drug discovery and preloaded in the server. D-Peptide Builder is freely accessible at http://dpeptidebuilder. quimica.unam.mx:4000/. It is also accessible through the open D-Tools platform (DIFACQUIM Tools for Chemoinformatics https://www.difacquim.com/d-tools/).

BIOFACQUIM: A Mexican Compound Database of Natural Products.

Compound databases of natural products have a major impact on drug discovery projects and other areas of research. The number of databases in the public domain with compounds with natural origins is increasing. Several countries, Brazil, France, Panama and, recently, Vietnam, have initiatives in place to construct and maintain compound databases that are representative of their diversity. In this proof-of-concept study, we discuss the first version of BIOFACQUIM, a novel compound database with natural products isolated and characterized in Mexico. We discuss its construction, curation, and a complete chemoinformatic characterization of the content and coverage in chemical space. The profile of physicochemical properties, scaffold content, and diversity, as well as structural diversity based on molecular fingerprints is reported. BIOFACQUIM is available for free.

Exploring the chemical space of peptides for drug discovery: a focus on linear and cyclic penta-peptides.

Peptide and peptide-like structures are regaining attention in drug discovery. Previous studies suggest that bioactive peptides have diverse structures and may have physicochemical properties attractive to become hit and lead compounds. However, chemoinformatic studies that characterize such diversity are limited. Herein, we report the physicochemical property profile and chemical space of four synthetic linear and cyclic combinatorial peptide libraries. As a case study, the analysis was focused on penta-peptides. The chemical space of the peptide and N-methylated peptides libraries was compared to compound data sets of pharmaceutical relevance. Results indicated that there is a major overlap in the chemical space of N-methylated cyclic peptides with inhibitors of protein-protein interactions and macrocyclic natural products available for screening. Also, there is an overlap between the chemical space of the synthetic peptides with peptides approved for clinical use (or in clinical trials), and to other approved drugs that are outside the traditional chemical space. Results further support that synthetic penta-peptides are suitable compounds to be used in drug discovery projects.

Protein-Protein Interaction Modulators for Epigenetic Therapies.

Targeting protein-protein interactions (PPIs) is becoming an attractive approach for drug discovery. This is particularly true for difficult or emerging targets, such as epitargets that may be elusive to drugs that fall into the traditional chemical space. The chemical nature of the PPIs makes attractive the use of peptides or peptidomimetics to selectively modulate such interactions. Despite the fact peptide-based drug discovery has been challenging, the use of peptides as leads compounds for drug discovery is still a valid strategy. This chapter discusses the current status of PPIs in epigenetic drug discovery. A special emphasis is made on peptides and peptide-like compounds as potential drug candidates.