Ossama Daoui

Making advancements in Quantitative Structure-Activity Relationship (QSAR) modeling is crucial for predicting biological activities in new compounds. Traditional 2D-QSAR and 3D-QSAR methods often face challenges in terms of computational efficiency and predictive accuracy. This study introduces a machine learning approach using 3D Discrete Tchebichef Moments (3D-DTM) to address these issues. The 3D-DTM method offers efficient computation, robust descriptor generation, and improved interpretability, making it a promising alternative to conventional QSAR techniques. By capturing global 3D shape information, this method provides better representation of molecular interactions essential for biological activities. We applied the 3D-DTM model to a dataset of 46 molecules derived from the Dihydrophenanthrene scaffold, screened against the enzymatic activity of 3-chymotrypsin-like protease, a key antiviral target. Principal Component Analysis and k-means clustering refined descriptors, followed by stepwise Multiple Linear Regression (step-MLR), Partial Least Squares Regression (PLS-R), and Feed-Forward Neural Network (FFNN) techniques for 3DTMs-QSAR model development. The results showed high correlation and predictive accuracy, with significant validation from internal and external tests. The step-MLR model emerged as the optimal method due to its balance of predictive power and simplicity. Validation through y-Randomization and applicability domain analysis confirmed the model’s robustness. Virtual screening of 100 novel compounds identified 32 with improved pIC₅₀ values. This study highlights the potential of 3D-DTMs in QSAR modeling, providing a scalable and reliable tool for computational chemistry and drug discovery. A user-friendly software tool was also developed to facilitate 3D-DTM extraction from input 3D molecular images.

Erythropoietin-producing hepatocellular carcinoma B4 (EphB4) belongs to the Eph family of receptor tyrosine kinases (RTKs) and plays a significant role in the amplification of many kinds of cancers such as lung cancer, head and neck cancer, and mesothelioma. In this work, we applied a fragment-based drug design strategy to find novel Ephb4 receptor inhibitors as potential therapeutic candidates. A screening of over 269,000 fragments from various libraries has been conducted to determine their affinity for binding EphB4. Using Schrödinger software, 1,000 fragments with the highest docking scores underwent fragment linking to generate 100 new molecules. The EphB4 binding affinity and ADMET characteristics of the top 20 docking score molecules were then examined in more detail. After the best compounds were selected, a molecular dynamics study was conducted to determine the stability of the ligand-receptor complex in the top three molecules. The resultant compound may be investigated further in the context of tyrosine kinase inhibitor drug development.

The electronic structure and optical properties of eight novel molecules based on 8-alkyl-8H-thieno[2,3-b] indole was determined using density functional theory DFT, TD-DFT and 3D-QSPR.

In this work, we investigated the quantitative relationship between biological activity against non-small cell lung cancer (NSCLC) and the molecular structure of a series of 38 cyclohexane-1,3-dione-dimidone derivatives. For this purpose, molecular descriptors calculated by DFT-B3LYP/6-31G, topological, and physicochemical analysis were used. The results of the evaluations of the quantitative activity structure relationship (QSAR) models developed in this work via Multiple Linear Regression and via Multiple Non-Linear Regression (MLR and MNLR) techniques indicate the high predictive power of these models, (R² = 0.913; R²_CV = 0.85, R²_test = 0.934) for the linear model and (R² = 0.991; R²_CV = 0.82; R²_test = 0.997) for the nonlinear model. Using predictions from the QSAR model, new molecular structures were designed, their activity against NSCLC was evaluated, and the most important interactions between these molecules and the human c-Met protein were predicted. The predictions from QSAR models, molecular docking, and an evaluation of the in silico ADMET properties suggested that 1 of the 16 newly designed molecules is a candidate that may be a drug for NSCLC.

The rapid spread of fatal diseases forces us to reconsider our position as researchers in the field of drug development assisted by computers. On the other hand, new drugs are constantly being developed from natural compounds using computer-aided methods. Virtual screening is carried out in accordance with the protocols established by accredited organizations and is based on the structure of the ligands or the structure of the target proteins. The formation of a database based on phytotherapy is the best option because Morocco is known for its wealth of plants and their traditional uses in medicine, which encourages us to better utilize our cultural heritage and the natural diversity of our country in the therapeutic field. The combination of these fields of CADD and phytotherapy can yield positive results in terms of the development of molecules with the goal of their use as drugs that are capable of inhibiting a pathological protein. In the present study, deep research was performed to collect a set of phytoconstituents extracted from Moroccan plants in order to evaluate their ability to limit the proliferation of SARS-CoV-2. Molecular docking was performed in the active sites of the 6lu7 and 6m0j proteins to assess their binding affinity. The structural stability of the target proteins was validated by redocking. The compounds with good binding affinity values were further subjected to Lipinski’s rule of five, chemical absorption and toxicity analysis. The results revealed that only two of the compounds presented a good binding affinity towards the target proteins and are able to be used as orally available medication for SARS-CoV-2.

Small molecules such as 4-phenoxypyridine derivatives have remarkable inhibitory activity against c-Met enzymatic activity and proliferation of cancer cell lines. Since there is a relationship between structure and biological activity of these molecules, these little compounds may have great potential for clinical pharmaceutical use against various types of cancer caused by c-Met activity. The purpose of this study was to remodel the structures of 4-phenoxypyridine derivatives to achieve strong inhibitory activity against c-Met and provide favorable pharmacokinetic properties for drug design and discovery. Therefore, this paper describes the structure-activity relationship and the rationalization of appropriate pharmacophore sites to improve the biological activity of the investigated molecules, based on bioinformatics techniques represented by a computer-aided drug design approach. Accordingly, robust and reliable 3D-QSAR models were developed based on CoMFA and CoMSIA techniques. As a result, 46 lead molecules were designed and their biological and pharmacokinetic activities were predicted in silico. Screening filters by 3D-QSAR, Molecular Docking, drug-like and ADME-Tox identified the computer-designed compounds P54 and P55 as the best candidates to achieve high inhibition of c-Met enzymatic activity compared to the synthesized template compound T14. Finally, through molecular dynamics simulations, the structural properties and dynamics of c-Met free and complex (PDB code: 3LQ8) in the presence of 4-phenoxypyridine-derived compounds in an aqueous environment are discussed. Overall, the rectosynthesis of the designed drug inhibitors (P54 and P55) and their in vitro and in vivo bioactivity evaluation may be attractive for design and discovery of novel drug effective to inhibit c-Met enzymatic activity. Graphical Abstract Communicated by Ramaswamy H. Sarma