Computational design of substrate selective inhibition

Synergizing Experimentation and Computation: Predicting Energetic Potential in New Cyclo-Peroxide Compounds

This manuscript explores the synthesis of new cyclo-peroxide compounds (CPs) through a systematic approach involving 10 different ketones and two concentrations of H2O2. Following spectroscopic analysis and calorimetric tests on 10 selected compounds, the percentage of Power Index (%PI) was calculated. The study introduces a computational methodology based on the Iterative Stochastic Elimination (ISE) algorithm. The newly constructed ISE model, with demonstrated robust predictive capabilities indicated by its statistical parameters, was employed to screen and score the CPs, assessing their potential as energetic materials. Comparison between %PI obtained experimentally, and the ISE index derived computationally revealed consistent assessments of the new CPs' energetic potential. The research emphasizes that, particularly in the synthesis of cyclic peroxides, the ISE model is a preferable and efficient tool for predicting a compound's potential as an energetic substance. Utilizing the ISE model ensures faster, more cost-effective, and safer decision-making in experimental examinations, focusing attention only on compounds with the highest ISE scores. Furthermore, the manuscript suggests an intriguing avenue for future research by proposing the investigation of ester nitrates. The study advocates a comprehensive approach that combines experimental methods (synthesis, spectroscopy, and DSC) with computational evaluation using the ISE model to identify potential high-energy compounds. This integrated approach promises to enhance the efficiency and reliability of the energetic materials discovery process.

Modeling High Energy Molecules and Screening to Find Novel High Energy Candidates

High energy materials (HEMs) play pivotal roles in diverse military and civil-commercial sectors, leveraging their substantial energy generation. Integrating machine learning (ML) into HEM research can expedite the discovery of high-energy compounds, complementing or replacing traditional experimental approaches. This manuscript presents an application of our in-house Iterative Stochastic Elimination (ISE) algorithm to identify HEMs. ISE is a generic algorithm that produces reasonable solutions for highly complex combinatorial problems. In molecular discovery, ISE focuses on physicochemical properties to distinguish between different classes of molecules. Due to its long track record in discovering novel, highly active biomolecules, we decided to apply ISE to another type of molecular discovery: High-energy materials. Two distinct ISE models, Model A (92 HEMs) and Model B (169 HEMs), integrated non-HEMs for comprehensive analysis. The results showcase significant achievements for both Models A and B. Model A identified 69% of active molecules in Model B, of which 62% had the highest score. Model B identified 80% of active molecules in Model A, with 61% having the highest score among those 80%. Subsequently, Model C was developed, merging all active molecules (261) from Models A and B. Statistical data indicate that Model C is a high-quality model. It was used to screen and score nearly 2 million molecules from the Enamine database. We find 66 molecules with the highest score of 0.89, plus 8 with that score which are active molecules included in the learning set of Model C. From the 66 molecules, 21 (32%) contain at least one nitro group. In conclusion, this study positions the ISE algorithm as a potential tool for discovering novel HEM candidates, offering a promising pathway for efficient and sustainable advancements in high-energy materials research.

Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions

Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.

In silico Docking Analysis for Blocking JUNO-IZUMO1 Interaction Identifies Two Small Molecules that Block in vitro Fertilization

Combined hormone drugs are the basis for orally administered contraception. However, they are associated with severe side effects that are even more impactful for women in developing countries, where resources are limited. The risk of side effects may be reduced by non-hormonal small molecules which specifically target proteins involved in fertilization. In this study, we present a virtual docking experiment directed to discover molecules that target the crucial fertilization interactions of JUNO (oocyte) and IZUMO1 (sperm). We docked 913,000 molecules to two crystal structures of JUNO and ranked them on the basis of energy-related criteria. Of the 32 tested candidates, two molecules (i.e., Z786028994 and Z1290281203) demonstrated fertilization inhibitory effect in both an in vitro fertilization (IVF) assay in mice and an in vitro penetration of human sperm into hamster oocytes. Despite this clear effect on fertilization, these two molecules did not show JUNO–IZUMO1 interaction blocking activity as assessed by AVidity-based EXtracellular Interaction Screening (AVEXIS). Therefore, further research is required to determine the mechanism of action of these two fertilization inhibitors.

MonteCarbo: A software to generate and dock multifunctionalized ring molecules

MonteCarbo is an open-source software to construct simple 5-, 6-, and 7-membered ring multifunctionalized monosaccharides and nucleobases and dock them into the active site of carbohydrate-active enzymes. The core bash script executes simple orders to generate the Z-matrix of the neutral molecule of interest. After that, a Fortran90 code based on a pseudo-random number generator (Monte Carlo method) is executed to assign dihedral angles to the different rotamers present in the structure (ring and rotating functional groups). The program also has a generalized internal coordinates (GIC) implementation of the Cremer and Pople puckering coordinates ring. Once the structures are generated and optimized, a second code is ready to execute in serial the docking of multiple conformers in the active site of a wide family of enzymes.

Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results

Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.