Robust QSAR prediction models for volume of distribution at steady state in humans using relative distance measurements

Synthesis, docking, MD simulation, ADMET, drug likeness, and DFT studies of novel furo[2,3-b]indol-3a-ol as promising Cyclin-dependent kinase 2 inhibitors

A new series of furo[2,3-b]indol-3a-ol derivatives was synthesized to investigate their potential as inhibitors of the Cyclin-dependent kinase 2 (CDK2) enzyme. CDK2 is a serine/threonine protein kinase belonging to a family of kinases involved in the control of the cell cycle. Based on results from clinical studies, it has been shown that overexpression of CDK2 may play a role in the development of cancer. In order to discover highly effective derivatives, a process of in silico screening was carried out. The obtained results revealed that compound 3f. had excellent binding energies. In this study, in silico screening was used to investigate protein-ligand interactions and assess the stability of the most favorable conformation. The methods utilized included molecular docking, density functional theory (DFT) calculations using the B3LYP/6-31++G(d,p) basis set in the gas phase, molecular dynamic (MD) simulation, as well as the evaluation of drug-likeness scores. The pharmacokinetic and drug-likeness properties of the novel furo[2,3-b]indol-3a-ol derivatives suggest that these compounds have the potential to be considered viable candidates for future development as anticancer drugs.

Application of multi-objective optimization in the study of anti-breast cancer candidate drugs

In the development of anti-breast cancer drugs, the quantitative structure-activity relationship model of compounds is usually used to select potential active compounds. However, the existing methods often have problems such as low model prediction performance, lack of overall consideration of the biological activity and related properties of compounds, and difficulty in directly selection candidate drugs. Therefore, this paper constructs a complete set of compound selection framework from three aspects: feature selection, relationship mapping and multi-objective optimization problem solving. In feature selection part, a feature selection method based on unsupervised spectral clustering is proposed. The selected features have more comprehensive information expression ability. In the relationship mapping part, a variety of machine learning algorithms are used for comparative experiments. Finally, the CatBoost algorithm is selected to perform the relationship mapping between each other, and better prediction performance is achieved. In the multi-objective optimization part, based on the analysis of the conflict relationship between the objectives, the AGE-MOEA algorithm is improved and used to solve this problem. Compared with various algorithms, the improved algorithm has better search performance.

DFT, ADMET and Molecular Docking Investigations for the Antimicrobial Activity of 6,6'-Diamino-1,1',3,3'-tetramethyl-5,5'-(4-chlorobenzylidene)bis[pyrimidine-2,4(1H,3H)-dione]

Heterocyclic compounds, including pyrimidine derivatives, exhibit a broad variety of biological and pharmacological activities. In this paper, a previously synthesized novel pyrimidine molecule is proposed, and its pharmaceutical properties are investigated. Computational techniques such as the density functional theory, ADMET evaluation, and molecular docking were applied to elucidate the chemical nature, drug likeness and antibacterial function of molecule. The viewpoint of quantum chemical computations revealed that the molecule was relatively stable and has a high electrophilic nature. The contour maps of HOMO-LUMO and molecular electrostatic potential were analyzed to illustrate the charge density distributions that could be associated with the biological activity. Natural bond orbital (NBO) analysis revealed details about the interaction between donor and acceptor within the bond. Drug likeness and ADMET analysis showed that the molecule possesses the agents of safety and the effective combination therapy as pharmaceutical drug. The antimicrobial activity was investigated using molecular docking. The investigated molecule demonstrated a high affinity for binding within the active sites of antibacterial and antimalarial proteins. The high affinity of the antibacterial protein was proved by its low binding energy (-7.97 kcal/mol) and a low inhibition constant value (1.43 µM). The formation of four conventional hydrogen bonds in ligand-protein interactions confirmed the high stability of the resulting complexes. When compared to known standard drugs, the studied molecule displayed a remarkable antimalarial activity, as indicated by higher binding affinity (B.E. -5.86 kcal/mol & Ki = 50.23 M). The pre-selected molecule could be presented as a promising drug candidate for the development of novel antimicrobial agents.

Evaluation of Prediction Accuracy for Volume of Distribution in Rat and Human Using In Vitro, In Vivo, PBPK and QSAR Methods

Volume of distribution at steady state (V_ss) is an important pharmacokinetic parameter of a drug candidate. In this study, V_ss prediction accuracy was evaluated by using: (1) seven methods for rat with 56 compounds, (2) four methods for human with 1276 compounds, and (3) four in vivo methods and three Kp (partition coefficient) scalar methods from scaling of three preclinical species with 125 compounds. The results showed that the global QSAR models outperformed the PBPK methods. Tissue fraction unbound (f_u,t) method with adipose and muscle also provided high V_ss prediction accuracy. Overall, the high performing methods for human V_ss prediction are the global QSAR models, Øie-Tozer and equivalency methods from scaling of preclinical species, as well as PBPK methods with Kp scalar from preclinical species. Certain input parameter ranges rendered PBPK models inaccurate due to mass balance issues. These were addressed using appropriate theoretical limit checks. Prediction accuracy of tissue Kp were also examined. The f_u,t method predicted Kp values more accurately than the PBPK methods for adipose, heart and muscle. All the methods overpredicted brain Kp and underpredicted liver Kp due to transporter effects. Successful V_ss prediction involves strategic integration of in silico, in vitro and in vivo approaches.

13C NMR chemical shift prediction of diverse chemical compounds

Selection of key descriptors is very important in QSPR analysis. Presence of noise in the subset of descriptors reduces the quality of predictions. A complete set is considered as perfect when it does not include irrelevant or redundant elements. This paper reports complete sets of descriptors used to develop QSPR models for 1786 ¹³C NMR chemical shifts (δ_C parameters) of carbon atoms in 125 diverse chemical compounds. PBE1PBE/6-311G(2d,2p) and B3LYP/6-31G(d) basis sets were used for quantum chemistry calculations after the molecular structures were optimized with semi-empirical AM1 and B3LYP/6-31G(d). The two complete sets consisting of magnetic shielding elements (σ_XX, σ_YY, σ_ZZ) and the chemical shift principal values (σ₁₁, σ₂₂, σ₃₃) were used as the inputs for support vector machine (SVM) models of δ_C parameters. The four SVM models obtained have the mean root mean square (rms) errors of about 4.5-4.6 ppm. The results suggest that SVM models are accurate and acceptable compared with previous models, although our models are based on a relatively large set of compounds. Our approach is valuable in the selection of important descriptors for QSPR studies of δ_C parameters.