Protein kinases are vital drug targets, yet designing selective inhibitors is challenging, compounded by resistance and kinome complexity. This review explores Quantitative Structure-Activity Relationship (QSAR) modeling for kinase drug discovery, focusing on integrating traditional QSAR with machine learning (ML)-CNNs, RNNs-and structural data. Methods include structural databases, docking, and deep learning QSAR. Key findings show ML-integrated QSAR significantly improves selective inhibitor design for CDKs, JAKs, PIM kinases. The IDG-DREAM challenge exemplifies ML's potential for accurate kinase-inhibitor interaction prediction, outperforming traditional methods and enabling inhibitors with enhanced selectivity, efficacy, and resistance mitigation. QSAR combined with advanced computation and experimental data accelerates kinase drug discovery, offering transformative precision medicine potential. This review highlights deep learning-enhanced QSAR's novelty in automating feature extraction and capturing complex relationships, surpassing traditional QSAR, while emphasizing interpretability and experimental validation for clinical translation.

Olive Mill Solid Residue (OMSR) can be utilized as a bio-sorbent in wastewater treatment. Even though several studies on OMSR as a bio-sorbent were carried out, there is still a need to investigate a simple and relatively inexpensive OMSR treatment that increases pollutant removal. In this study; OMSR is used in batch experiments to remove toxic heavy metals from aqueous solutions including Cd²⁺, Cu²⁺, and Pb²⁺ ions. The effect of OMSR treatment (untreated; OMSR-U, treated with n-hexane; OMSR-H, and treated with water; OMSR-W) was investigated by chemical oxygen demand and cation exchange capacity. It was confirmed by both tests that OMSR-W was the best treatment. The same result was re-confirmed by batch uptake experiments of the heavy metal ions. Using OMSR-W as a bio-sorbent; the effect of several parameters such as pH, contact time, bio-sorbent concentration, metal ions concentration, and the presence of other metal species were studied to figure their influence on the metal ions uptake. The optimum conditions for single metal systems were found to occur at pH 5.5, an initial metal concentration of 50 mg/L, a shaking time of 60 minutes, a bio-sorbent concentration of 20 g/L. In binary metal ions solutions; Cd²⁺ uptake was increased in presence of Cu²⁺ or Pb²⁺. However, the uptake of Cu²⁺ and Pb²⁺ was decreased in presence of other metals. The equilibrium sorption data for single metal systems were described by the Langmuir isotherm model. The highest value of maximum uptake was found for Pb²⁺ (4.587 mg/g) followed by Cd²⁺ (4.525 mg/g) and Cu²⁺ (4.367 mg/g). These results show that OMSR-W, which has a very low economical value, could be used for the treatment of wastewater contaminated with heavy metals.

Classical trajectory calculations for various atom-diatomic molecules were preformed using the three-dimensional Monte Carlo method. The reaction probabilities, cross-sections and rate constants of several systems were calculated. Equations of motion, which predict the positions and momenta of the colliding particles after each step, have been integrated numerically by the Runge-Kutta-Gill and Adams-Moulton methods. Morse potential energy surfaces were used to describe the interaction between the atom and each atom in the diatomic molecules. The results were compared with experimental ones and with other theoretical values. Good agreement was obtained between calculated rate constants and those obtained experimentally. Also, reasonable agreement was observed with theoretical rate constants obtained by other investigators using different calculation methods. The effects of the temperature, the nature of the colliding particles and the thermochemistry were studied. The results showed a strong dependence of the reaction rates on these factors.