A practical guide and a warning for researchers who are designing metallodrugs by using docking is offered, concentrating on the application of inorganic complexes in the field of anticancer.
Abstract
The use of molecular docking in structure-based drug design is an indispensable tool in the field of computational chemistry; however, the application of molecular docking to metal complexes (such as Pt, Pd, Au, and Ag) poses unique challenges because of the variety of coordination geometries and electronic properties of metal complexes that are not represented by the typical force field. The literature (2010–2025) was reviewed and summarized in a peer-reviewed manner, concentrating on the application of inorganic complexes in the field of anticancer. The 13 representative case studies were identified by a systematic search of PubMed, Scopus and Web of Science and analysed for binding energies, RMSD values, software protocols and metal-specific parameterisation strategies. Majority of the studies used AutoDock4, AutoDock Vina or MOE with binding energies ranging from − 96.35 to − 1.48 kcal·mol⁻¹. Only three studies reported RMSD values < 2.0 Å, which is considered to be reliable pose prediction. The major drawbacks are: (i) absence of built-in metal coordination bond support in standard software; (ii) reporting of grid parameters and force field versions varies from one software to another; and (iii) very little quantum mechanical pre-optimization. Specialized tools (MetalDock, MCPB.py) increase accuracy, but are very time consuming to parameterise manually. Parameterisation, experimental validation (XRD, NMR, bioassays) and transparency of reporting are essential for reliable docking of metal complexes. Machine learning scoring functions are expected to be combined with quantum mechanical methods in the future. This review offers a practical guide and a warning for researchers who are designing metallodrugs by using docking.
Two novel triazine-based scaffolds, 9a and 9b, were synthesized through sequential substitution on cyanuric chloride. The resulting compounds were characterized using 1H NMR, 13C NMR, IR, UV-Vis, and Mass spectrometry. Subsequently, target prediction was performed to identify their potential biological targets using the SwissTargetPrediction tool. To explore the mechanism of action of these synthesized compounds and identify the putative drug target, molecular docking calculations were performed on the seven shortlisted druggable targets. Based on docking studies, mammalian target of rapamycin (mTOR) was selected as the target for the synthesized molecules 9a and 9b with docking scores of -7.367 kcal/mol and -9.022 kcal/mol, respectively. Extensive molecular dynamics simulations were conducted on the docked complexes to understand the dynamic behaviour. Moreover, molecules, 9a and 9b were also subjected to density functional theory (DFT) calculations to analyse the Molecular electrostatic potential and study the electronic density distribution over the scaffolds of these molecules. Subsequent biological evaluation in MCF-7 (cancerous), HeLa (cervical cancer cells), HepG2 (liver cancer cells) and WS1 (non-cancerous) cell lines were performed. Collectively across all studies, it was concluded that 9b is a promising antiproliferative agent, with an IC50 of 90 μM against all three cancer cell lines. Furthermore, compound 9b exhibited little to no cytotoxicity towards the WS1 cell line, indicating an acceptable safety profile and selective cytotoxicity against cancer cells. Hence, these findings highlight the potential of 9b as a promising anticancer lead candidate for further structural optimisation and therapeutic development.
Aayushi Joshi, Abhishek Sharma, Giftson J. Senapathy et al.· Biochemical and Biophysical...· 0 citations
Molecular docking simulation of 3 molecules of N-(1H-benzo[d]imidazol-2-yl)-7-chloro-6-fluorobenzo[d]thiazol2-amine with the target receptor aromatase was carried out so as to evaluate their theoretical binding affinities. The
chemical structure of the molecules was accurately drawn using ChemDraw Ultra software, then optimized at density
functional theory (DFT) using Becke’s three-parameter Lee–Yang–Parr hybrid functional (B3LYP/6-311**)
basis set in a vacuum of Spartan 14 software. Subsequently, the docking operation was carried out using PyRx
virtual screening software. Three compounds were developed by fusion of a benzothiazole and benzimidazole
ring system targeting the treatment of breast cancer. The marketed drug Exemestane which is also an aromatase
inhibitor was used as a reference molecule for docking. The highest docking score from all three molecules was
-8.6 kcal/mol which is very near to the standard drug score -9.5 kcal/mol which suggests that the compound
exhibited favorable protein–ligand interactions, indicating its potential as a lead candidate for further
investigation. In-silico ADME and drug-likeness prediction of the molecules showed good pharmacokinetic
properties having high gastrointestinal absorption, orally bioavailable, and less toxic. The outcome of the present
research strengthens the relevance of these compounds as promising lead candidates for the treatment of breast
cancer which could help the medicinal chemists and pharmaceutical professionals in further designing and
synthesis of more potent drug candidates. Moreover, the research also encouraged the in vivo and in vitro
evaluation study for the proposed designed compounds to validate the computational findings.
Khyati Bhagdev, Chintankumar J. Tank· International Journal of Dru...· 0 citations
Molecular docking is one of the most established methods in computational drug discovery, due to its balance of speed and accuracy. However, the accuracy of docking results depends on a number of different parameters, and systematic reference data for comparisons to more advanced methods for binding affinity prediction are still scarce. This study assesses the impact of key parameters on the accuracy of binding free energy estimates from docking, using nine benchmark systems with 278 high-affinity ligands. Using the Molecular Operating Environment (MOE), we evaluated combinations of three receptor structures (two crystal structures, one AlphaFold2 model), two force fields, two scoring functions, two receptor flexibility settings, and two statistical evaluation schemes. The performance of the docking approaches is measured based on the squared Pearson’s correlation coefficient (R²), the root mean square error (RMSE) with respect to the experimental binding affinities, as well as the mean signed error (MSE) and Kendall’s tau for individual targets and the full dataset. The results show that the scoring function and the protein structure are the most important factors for binding affinity accuracy in rigid docking with the MOE software. Amber10:EHT and MMFF94x force fields had the same average Rmean2 value, but Amber10:EHT had a lower average RMSEmean. AlphaFold2 protein models yielded lower binding affinity accuracy and higher errors compared to experimental crystal structures, although induced fit docking improved results. Using the original benchmark, we also compared several docking programs. DOCK6 and MOE performed best, with mean R² values of about 0.49 and 0.40, respectively. The remaining docking programs did not outperform a molecular weight regression baseline. For a subset of four targets (CDK2, JNK1, P38, TYK2) evaluated in previous work, the performance of the optimized DOCK6 and MOE protocols produced correlation coefficients similar to those reported for certain MM/PBSA, FMO, and Boltz2 implementations evaluated on the same target subset. This raises questions about potential dataset biases, the structural preparation, or the implementation of those methods. Docking therefore should be considered as an important and computationally inexpensive reference baseline for binding affinity prediction.
Konstantinos Tornesakis, J. Essex, Paul A. Cox et al.· Journal of Computer-Aided Mo...· 0 citations
Structural elucidation is an important step in chemical, pharmaceutical, biochemical, and natural product research because it helps researchers identify a compound and understand how its atoms are arranged. Traditional techniques such as nuclear magnetic resonance spectroscopy, mass spectrometry, infrared spectroscopy, ultraviolet spectroscopy, and X-ray crystallography remain the main methods used for structure determination. However, these methods may sometimes face difficulties, especially when compounds have closely related structures, are unstable, or are obtained in very small quantities. Molecular docking has become a useful computational tool that can support structural elucidation by providing information on how an identified or proposed compound may interact with a biological target. It does not replace experimental methods, but it can help explain the possible relationship between chemical structure and biological activity. In docking studies, a ligand is placed into the binding site of a protein to predict its binding pose, interaction pattern, and possible binding strength. This is useful in natural product research, drug discovery, and studies involving bioactive compounds, where researchers often need to understand how a compound may produce its observed activity. Molecular docking can also support the comparison of related compounds, interpretation of structure–activity relationships, and selection of promising molecules for further testing. Despite its usefulness, docking has limitations, including issues with protein flexibility, scoring accuracy, ligand preparation, and binding site selection. Therefore, docking results should be interpreted carefully and supported with experimental data, validation methods, molecular dynamics simulation, and biological assays where possible. This review discusses the principles, workflow, applications, limitations, and future relevance of molecular docking as a complementary tool in structural elucidation.
Akorede Ajibola, R. Lawal, Esther Ojumah et al.· Zeugma Biological Science· 0 citations
We have compared the performance of 64 different computational methods, based on combined quantum mechanical (QM) and molecular mechanical (QM/MM) or QM-cluster calculations in a continuum solvent, to estimate the acid constant (pK a) of metal-bound ligands in proteins. As a calibration set, we use 12 experimental pK a values from six different proteins that involve Zn2+, Fe3+, or Fe4+. We employ two different density functional theory (DFT) methods (TPSS and B3LYP), two basis sets (def2-SV(P) and def2-TZVPD), QM regions of three different sizes (∼40, ∼100, and ∼350 atoms), relaxed or fixed surroundings, and three different values of the dielectric constant of the continuum-solvation model (ε = 4, 20, or 80). The results clearly show that QM-cluster+continuum-solvation is much better than QM/MM. In general, the most accurate results are obtained with ε = 80 and the minimal QM region. The two DFT methods, the two basis sets, and relaxing or fixing the surroundings give similar results. The best-performing method is TPSS with the minimal QM region, def2-TZVPD, relaxed surroundings, and ε = 80, yielding a mean absolute deviation (after removal of a systematic error of 11.6 pK a units, pu) of 2.0 pu and a maximum deviation of 5.0 pu. The coefficient of determination (R 2) and Kendall’s τ with respect to the experimental pK a values are both 0.64, while Spearman’s rank correlation coefficient is 0.78. This level of accuracy should be sufficient to reliably determine the protonation states of metal-bound ligands in QM-based studies of enzymatic reaction mechanisms.
Maryam Haji Dehabadi, Mehdi Irani, S. Jafari et al.· Journal of Chemical Theory a...· 0 citations