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Artificial intelligence (AI) is being used in oncological drug development to address the high costs, low success rates, and long timelines that characterize traditional drug development pipelines. The use of machine learning (ML) and deep learning (DL) models in computer-aided drug design is constantly growing owing to their capacity to analyze large, heterogeneous datasets, their ability to capture nonlinear biological trends, and their integration of various molecular and clinical characteristics. AI applications accelerate target discovery by predicting protein structures, ranking disease-relevant genes, and assessing target drugability. AI can be used to conduct rapid searches of multiplexed chemical libraries, predict drug-target interactions, and optimize the pharmacological and physicochemical properties of drugs in virtual screening. Advanced neural network designs also aid in de novo drug design, which involves developing new molecular structures with therapeutic properties of interest. This review outlines how AI has been used for target identification, virtual screening, de novo molecular design, and, specifically, in cancer applications. It further discusses the major issues in AI-based drug development, such as data quality, model interpretation, computational constraints, and ethical and regulatory considerations, which remain essential obstacles to broader clinical translation. Show less

Biomedical research benefits from the rapid growth and diversity of experimentally detected protein-protein interactions (PPIs) by gaining important biological insights. However, increasingly dense PPI networks can be challenging to interpret and apply. The 2025 update of the Integrated Interactions Database (IID) enhances accessibility and utility through several new features. We identify and incorporate network structural components from co-purified protein sets, as well as curated and predicted complexes, enabling users to explore network organization beyond binary interactions. Functional, pathway, and disease associations of these components can be analyzed, enabling interactions to be grouped into higher-order structures with known or provisional biological roles. Users can now filter interactions by five detection types: pairwise, co-purification, colocalization, proximity, and other evidence. To extend the value and information of predicted interactions, we include interaction interface predictions for 53 647 PPIs, generated using the MEGADOCK docking algorithm, adding molecular detail for structural biology and variant impact studies. Finally, we map PPIs to 15 immune cell types and 12 additional normal tissues, offering tissue-specific views of interaction networks increasingly relevant in disease and immunology research. IID 2025 now includes over 1 million experimentally detected human PPIs, representing an 83% increase from the previous release, alongside expanded non-human networks. The portal remains publicly available at https://ophid.utoronto.ca/iid. Show less

Computational metabolomics will be established in drug discovery and research on complex biological networks. This field of research enhances the detection of metabolic biomarkers and the prediction of molecular interactions by combining multiscale analysis with in silico and molecular docking methods. These include nuclear magnetic resonance, mass spectrometry, and innovative bioinformatics, which enable the accurate generation and characterization of metabolomes. Molecular docking is a crucial tool for simulating the interaction between ligands and receptors, thereby facilitating the identification of potential therapeutics. It also discusses the potential of metabolomics to inform drug modes of action, from pharmacokinetics to forecasting toxicity, thereby streamlining drug development pipelines. We highlight applications in anticancer, antimicrobial, and antiviral drug discovery and explain how these computational models can accelerate target validation and enhance the accuracy of therapeutic strategies. In addition, this review addresses the current challenges and future directions for computational techniques in conjunction with experimental data to advance personalized medicine. In conclusion, this review aims to highlight the prospective approaches of computational metabolomics and molecular docking that identify evolutionary adaptive metabolisms of multiscale biological systems through their synergistic utilization to overcome the key hurdles involved in both drug discovery and metabolomic research. Show less

ABSTRACTSolanum erianthum D. Don (Solanaceae) species has wide range of usage in treating disease in folk medicine. This study focused on the isolation and characterization of Phytol, βCaryophyllene (βc), and Methoxy ‐ 4‐ Quercetin (M4Q) from Solanum erianthum leaf and seed fractions. These three compounds were studied for antioxidant activities using DPPH, SOD, and FRAP assays, accompanied by molecular docking, molecular dynamics, and ADMET tools. Our results showed that, Methoxy 4‐Quercetin scored good antioxidant potential in the studied assays, with a stable conformation and favorable properties. Docking analysis was used to determine the binding energies of these compounds with different receptor proteins and ligand complexes, as well as their stability, conformation, and binding energy in molecular dynamics. Pharmacokinetic compounds exhibit kinetic values and drug‐like characteristics, indicating their biological activity. Competent studies on the compounds using in silico analyses showed that all compounds have notable anti‐cardiotoxic and anti‐inflammatory effects. Thus, studies on phytocompounds as effective leads to the improvement of anti‐inflammatory and anti‐cardio agents are necessary for further in vivo studies. Show less

The emergence of new Mycobacterium tuberculosis (Mtb) strains resistant to the key drugs currently used in the clinic for tuberculosis treatment can substantially reduce the probability of therapy success, causing the relevance and importance of studies on the development of novel potent antibacterial agents targeting different vulnerable spots of Mtb. In this study, 28,860 compounds from the library of bioactive molecules were screened to identify novel potential inhibitors of β-ketoacyl-acyl carrier protein synthase I (KasA), one of the key enzymes involved in the biosynthesis of mycolic acids of the Mtb cell wall. In doing so, we used a structure-based virtual screening approach to drug repurposing that included high-throughput docking of the C171Q KasA enzyme with compounds from the library of bioactive molecules including the FDA-approved drugs and investigational drug candidates, assessment of the binding affinity for the docked ligand/C171Q KasA complexes, and molecular dynamics simulations followed by binding free energy calculations. As a result, post-modeling analysis revealed 6 top-ranking compounds exhibiting a strong attachment to the malonyl binding site of the enzyme, as evidenced by the values of binding free energy which are significantly lower than those predicted for the KasA inhibitor TLM5 used in the calculations as a positive control. In light of the data obtained, the identified compounds are suggested to form a good basis for the development of new antitubercular molecules of clinical significance with activity against the KasA enzyme of Mtb.Communicated by Ramaswamy H. Sarma. Show less

Copper(II), manganese(II), and mercury(II) complexes of 4-amino-5-(2-(1-pyridine-2-yl)ethylidene)hydrazinyl)-4H-1,2,4-triazole-3-thiol (H2TAP) were synthesized and characterized using CHN analysis, FT-IR, 1H-NMR, 13C-NMR, UV–Vis, ESR, MS, PXRD, magnetic moment measurements, molar conductance, and TG/DTA. DFT calculations indicate octahedral geometries and the neutral bidentate or tridentate chelating behavior of the ligand. Cyclic voltammetry revealed the complexes’ redox properties, and Job’s method elucidated stoichiometric compositions in solution. Biochemical assays demonstrated antimicrobial activity against Escherichia coli, Staphylococcus aureus, and Candida albicans. The MnII complex exhibited potent antitumor activity against HepG-2 cells. Antioxidant and DNA binding studies showed promising results, with docking investigations indicating strong interactions between the ligand/complexes and target proteins (PDB: 1YWN) and DNA (PDB: 8EC1), suggesting therapeutic potential. Show less

Neurological disorders are the leading cause of a large number of mortalities and morbidities. Nitrogen heterocyclic compounds have been pivotal in exhibiting wide array of therapeutic applications. Among them, tetrazole is a ubiquitous class of organic heterocyclic compounds that have attracted much attention because of its unique structural and chemical properties, and a wide range of pharmacological activities comprising anti-convulsant effect, antibiotic, anti-allergic, anti-hypertensive to name a few. Owing to significant chemical and biological properties, the present review aimed at highlighting the recent advances in tetrazole derivatives with special emphasis on their role in the management of neurological diseases. Besides, in-depth structure-activity relationships, molecular docking studies, and associated modes of action of tetrazole derivatives evident in in vitro, in vivo preclinical, and clinical studies have been discussed. Show less

The development of targeted chemotherapeutic agents against colorectal cancer (CRC), one of the most common cancers with a high mortality rate, is in a constant need. Nannocystins are a family of myxobacterial secondary metabolites featuring a 21-membered depsipeptide ring. The in vitro anti-CRC activity of natural and synthetic nannocystins was well documented, but little is known about their in vivo efficacy and if positive, the underlying mechanism of action. In this study we synthesized a nitroaromatic nannocystin through improved preparation of a key fragment, and characterized its in vitro activity and in vivo efficacy against CRC. We first described the total synthesis of compounds 2-4 featuring Heck macrocyclization to forge their 21-membered macrocycle. In a panel of 7 cancer cell lines from different tissues, compound 4 inhibited the cell viability with IC values of 1-6 nM. In particular, compound 4 (1, 2, 4 nM) inhibited the proliferation of CRC cell lines (HCT8, HCT116 and LoVo) in both concentration and time dependent manners. Furthermore, compound 4 concentration-dependently inhibited the colony formation and migration of CRC cell lines. Moreover, compound 4 induced cell cycle arrest at sub-G1 phase, apoptosis and cellular senescence in CRC cell lines. In three patient-derived CRC organoids, compound 4 inhibited the PDO with IC values of 3.68, 28.93 and 11.81 nM, respectively. In a patient-derived xenograft mouse model, injection of compound 4 (4, 8 mg/kg, i.p.) every other day for 12 times dose-dependently inhibited the tumor growth without significant change in body weight. We conducted RNA-sequencing, molecular docking and cellular thermal shift assay to elucidate the anti-CRC mechanisms of compound 4, and revealed that it exerted its anti-CRC effect at least in part by targeting AKT1. Show less

Despite the proven potential of metal complexes as therapeutics, the lack of computational tools available for the high-throughput screening of their interactions with proteins is a limiting factor toward clinical developments. To address this challenge, we introduce MetalDock, an easy-to-use, open access docking software for docking metal complexes to proteins. Our tool integrates the AutoDock docking engine with three well-known quantum software packages to automate the docking of metal-organic complexes to proteins. We used a Monte Carlo sampling scheme to obtain the missing Lennard-Jones parameters for 12 metal atom types and demonstrated that these parameters generalize exceptionally well. Our results show that the poses obtained by MetalDock are highly accurate, as they predict the binding geometries experimentally determined by crystal structures with high spatial reproducibility. Three different case studies are presented that demonstrate the versatility of MetalDock for the docking of diverse metal-organic compounds to different biomacromolecules, including nucleic acids. Show less

Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. The prediction of drug-target interactions is an essential part of the DD process because it can accelerate it and reduce the required costs. DTI prediction performed in silico have used approaches based on molecular docking simulations, including similarity-based and network- and graph-based ones. This paper presents MPS2IT-DTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for encoding molecule and protein sequences onto images; the definition of a deep-learning approach based on a convolutional neural network in order to create a new method for DTI prediction. Training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. With the Davis dataset, we obtained 0.876 for the concordance index and 0.276 for the MSE; with the KIBA dataset, we obtained 0.836 and 0.226 for the concordance index and the MSE, respectively. Moreover, the MPS2IT-DTI model represents molecule and protein sequences as images, instead of treating them as an NLP task, and as such, does not employ an embedding layer, which is present in other models. Show less

Two new coordination complexes of Cu(II) and Mn(II), viz., [Cu(bpy)(H2O)4]SO4·2H2O (1) and [Mn(4-CNpy)2(H2O)3SO4]·H2O (2) (bpy = 2,2′-bipyridine, 4-CNpy = 4-cyanopyridine), have been synthesized and characterized by using single crystal X-ray diffraction, elemental analysis, FT-IR spectroscopy, electronic spectroscopic techniques and TGA. The crystal structure of 1 uncovers the formation of sulfate–water assemblies involving lattice and coordinated water molecules, while complex 2 reveals the presence of unconventional weak T-shaped CN⋯CN contacts in the layered architecture. We have analysed the unconventional interesting interactions using DFT calculations, molecular electrostatic potential (MEP), the NCI plot and QTAIM computational tools. The interaction energies of the two H-bonded dimers in 1 are very large because of the coulombic attraction between the dicationic H-bonded donor and the dianionic acceptor. It is interesting to observe that despite the energy of the H-bonds being very small compared to the total dimerization energy, the final geometry of the assembly in 1 is due to the charge assisted directional H-bonds instead of the non-directional ion-pair interactions. The DFT study reveals that the T-shaped CN⋯CN interaction in 2 is very weak, in good agreement with the small MEP energy at the nitrile carbon atom. Anticancer studies of the compounds have been carried out using Dalton's lymphoma cell line using MTT and apoptosis assay. The results of compound 1 and 2 mediated cell cytotoxicity on the DL cancer cell line showed a significant concentration-dependent reduction in cell viability, while negligible cytotoxicity was observed in normal (PBMC) cells. The docking simulation results also confirm the interaction of the complexes with the active sites of amino acids of the target proteins. Furthermore, pharmacophore models (2D and 3D) for the compounds were mapped to the H-bond donor, positive ionisable area and hydrophobic features that are important for establishing biological activities. No hematotoxicity was recorded for the compounds after treatment in normal mice. Show less

Three novel complexes, namely [Nd·L1·HCOO·(H2O)4] (1), [Pr·L1·HCOO·(H2O)4] (2) and [In·L2·Cl·(H2O)2] (3) (L1 = 1,1‐bis(5‐(pyrazin‐2‐yl)‐1,2,4‐triazol‐3‐yl)methane, L2 = 1,1‐bis(5‐(pyrazin‐2‐yl)‐1,2,4‐triazol‐3‐yl)ketone), were synthesized and characterized. The molecular structures of 1–3 were confirmed using single‐crystal X‐ray diffraction. All three obtained complexes are zero‐dimensional and connected to each other by hydrogen bonds. In 1 and 2 the metal is surrounded by nine donors and 3 has seven coordination sites. The interaction of 1–3 with calf thymus DNA (CT‐DNA) was explored using UV absorption spectra and fluorescence spectra. The intrinsic binding constants of 1–3 with CT‐DNA are about 1.9 × 104, 1.4 × 104 and 1.1 × 104, respectively. Stern–Volmer quenching plots of 1–3 have slopes of 0.1508, 0.134 and 0.1205, respectively. The ability of these complexes to cleave pBR322 plasmid DNA was demonstrated using gel electrophoresis assay. Apoptosis studies of the three novel complexes showed a significant inhibitory effect on HeLa cells. Furthermore, MTT assays were used to evaluate the anticancer activity of the three complexes. The cytotoxicity study indicated that complex 1 possesses a higher inhibitory rate of HeLa cells than the other complexes. Especially, the efficacy of 1 was shown to be the highest for cisplatin at 24 h. A further molecular docking technique was introduced to understand the binding of the complexes toward the target DNA. Show less

A series of N-benzoylated mononuclear copper(II) complexes of the type [Cu(L1−6)Cl2] (1–6), where L1= ethyl 4-benzoyl-5-methyl-7-aryl-4,7-dihydrotetrazolo[1,5-a]pyrimidine-6-carboxylate, L2= ethyl 4-(4-nitrobenzoyl)-5-methyl-7-aryl-4,7-dihydrotetrazolo[1,5-a]pyrimidine-6-carboxylate, L3 = ethyl 4-benzoyl-5-methyl-7-(4-methoxyphenyl)-4,7-dihydrotetrazolo[1,5-a]pyrimidine-6-carboxylate, L4 = ethyl 4-(4-nitrobenzoyl)-5-methyl-7-(4-methoxyphenyl)-4,7-dihydrotetrazolo[1,5-a]pyrimidine-6-carboxylate, L5 = ethyl 4-benzoyl-5-methyl-7-(4-chlorophenyl)-4,7-dihydrotetrazolo[1,5-a]pyrimidine-6-carboxylate and L6 = ethyl 4-(4-nitrobenzoyl)-5-methyl-7-(4-chlorophenyl)-4,7-dihydrotetrazolo[1,5-a]pyrimidine-6-carboxylate have been synthesized and characterized by spectral methods. Electron paramagnetic resonance spectra of complexes show four lines, characteristic of square planar geometry. The binding studies of the complexes with calf thymus DNA (CT–DNA) revealed groove mode of binding, which were further supported by molecular docking studies. Gel electrophoresis experiments demonstrated the ability of the complexes to cleave plasmid DNA in the absence of activators. Further, the cytotoxicity activity of the complexes were examined on three cancerous cell lines (lung (A549), cervical (HeLa) and colon (HCT-15)), and on two normal cells (human embryonic kidney (HEK) and peripheral blood mononuclear cells (PBMC)) by MTT assay. Show less

Automated docking is one of the most important tools for structure‐based drug design that allows prediction of ligand binding poses and also provides an estimate of how well small molecules fit in the binding site of a protein. A new scoring function based on AutoDock and AutoDock Vina has been introduced. The new hybrid scoring function is a linear combination of the two scoring function components derived from a multiple linear regression fitting procedure. The scoring function was built on a training set of 2412 protein–ligand complexes from pdbbind database (www.pdbbind.org.cn, version 2012). A test set of 313 complexes that appeared in the 2013 version was used for validation purposes. The new hybrid scoring function performed better than the original functions, both on training and test sets of protein–ligand complexes, as measured by the non‐parametric Pearson correlation coefficient, R, mean absolute error (MAE), and root‐mean‐square error (RMSE) between the experimental binding affinities and the docking scores. The function also gave one of the best results among more than 20 scoring functions tested on the core set of the pdbbind database. The new AutoDock hybrid scoring function will be implemented in modified version of AutoDock. Show less

Abstract A pharmaceutical drug compound is usually a small organic molecule, also termed as ligand, that binds to the target protein and alters the natural activity of the protein, thus, leading to a therapeutic effect. Computational docking or computer‐aided docking is an extremely useful tool to gain an understanding of protein–ligand interactions which is important for the drug discovery. Computational docking is the process of computationally predicting the placement and binding affinity of the ligand in the binding pocket of the protein. Docking methods rely on a search algorithm which computes the placement of the ligand in the binding pocket and a scoring function which estimates the binding affinity, that is, how strongly the ligand interacts with the protein. A variety of methods have been developed to solve the computational docking problems that range from simple point‐matching algorithms to explicit physical simulation methods. Key Concepts: Computational docking methods play an important role in the drug discovery process. A docking method computes the placement of a ligand in the binding pocket of a protein and estimates the binding affinity. Rigid‐body docking methods treat both the protein and ligand as rigid bodies. Flexible ligand methods treat the ligand as a flexible molecule and flexible receptor methods treat both the ligand and the protein as flexible molecules. Two main features of computational docking techniques are a conformation search algorithm and a scoring function that estimates binding affinity. Most of the computational docking programs treat the protein as a rigid molecule and the ligand as a flexible molecule. Protein flexibility is an important determinant of the accuracy of docking programs. Efforts have been made to account for protein flexibility in docking methods, but more needs to be done. Show less

AbstractMolecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein–ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time‐consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein–ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein–ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein–ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5. © 2010 Wiley Periodicals, Inc. J Comput Chem 32: 568–581, 2011 Show less

AbstractWe describe a scoring and modeling procedure for docking ligands into protein models that have either modeled or flexible side‐chain conformations. Our methodical contribution comprises a procedure for generating new potentials of mean force for the ROTA scoring function which we have introduced previously for optimizing side‐chain conformations with the tool IRECS. The ROTA potentials are specially trained to tolerate small‐scale positional errors of atoms that are characteristic of (i) side‐chain conformations that are modeled using a sparse rotamer library and (ii) ligand conformations that are generated using a docking program. We generated both rigid and flexible protein models with our side‐chain prediction tool IRECS and docked ligands to proteins using the scoring function ROTA and the docking programs FlexX (for rigid side chains) and FlexE (for flexible side chains). We validated our approach on the forty screening targets of the DUD database. The validation shows that the ROTA potentials are especially well suited for estimating the binding affinity of ligands to proteins. The results also show that our procedure can compensate for the performance decrease in screening that occurs when using protein models with side chains modeled with a rotamer library instead of using X‐ray structures. The average runtime per ligand of our method is 168 seconds on an Opteron V20z, which is fast enough to allow virtual screening of compound libraries for drug candidates. Proteins 2009. © 2008 Wiley‐Liss, Inc. Show less

AbstractA major problem in virtual screening concerns the accuracy of the binding free energy between a target protein and a putative ligand. Here we report an example supporting the outperformance of the AutoDock scoring function in virtual screening in comparison to the other popular docking programs. The original AutoDock program is in itself inefficient to be used in virtual screening because the grids of interaction energy have to be calculated for each putative ligand in chemical database. However, the automation of the AutoDock program with the potential grids defined in common for all putative ligands leads to more than twofold increase in the speed of virtual database screening. The utility of the automated AutoDock in virtual screening is further demonstrated by identifying the actual inhibitors of various target enzymes in chemical databases with accuracy higher than the other docking tools including DOCK and FlexX. These results exemplify the usefulness of the automated AutoDock as a new promising tool in structure‐based virtual screening. Proteins 2006. © 2006 Wiley‐Liss, Inc. Show less

AbstractThe early phases of commercial drug discovery programs are increasingly guided by information extracted from three‐dimensional structures of the target proteins and in silico design techniques. This review addresses key issues of docking and scoring, a popular technique in structure‐based drug design. The pros and cons of computational tools currently used will be outlined as well as the integration of these methods in the lead finding and lead optimization process. Show less

AbstractEight docking programs (DOCK, FLEXX, FRED, GLIDE, GOLD, SLIDE, SURFLEX, and QXP) that can be used for either single‐ligand docking or database screening have been compared for their propensity to recover the X‐ray pose of 100 small‐molecular‐weight ligands, and for their capacity to discriminate known inhibitors of an enzyme (thymidine kinase) from randomly chosen “drug‐like” molecules. Interestingly, both properties are found to be correlated, since the tools showing the best docking accuracy (GLIDE, GOLD, and SURFLEX) are also the most successful in ranking known inhibitors in a virtual screening experiment. Moreover, the current study pinpoints some physicochemical descriptors of either the ligand or its cognate protein‐binding site that generally lead to docking/scoring inaccuracies. Proteins 2004. © 2004 Wiley‐Liss, Inc. Show less

AbstractProtein‐based virtual screening of chemical libraries is a powerful technique for identifying new molecules that may interact with a macromolecular target of interest. Because of docking and scoring limitations, it is more difficult to apply as a lead optimization method because it requires that the docking/scoring tool is able to propose as few solutions as possible and all of them with a very good accuracy for both the protein‐bound orientation and the conformation of the ligand. In the present study, we present a consensus docking approach (ConsDock) that takes advantage of three widely used docking tools (Dock, FlexX, and Gold). The consensus analysis of all possible poses generated by several docking tools is performed sequentially in four steps: (i) hierarchical clustering of all poses generated by a docking tool into families represented by a leader; (ii) definition of all consensus pairs from leaders generated by different docking programs; (iii) clustering of consensus pairs into classes, represented by a mean structure; and (iv) ranking the different means starting from the most populated class of consensus pairs. When applied to a test set of 100 protein–ligand complexes from the Protein Data Bank, ConsDock significantly outperforms single docking with respect to the docking accuracy of the top‐ranked pose. In 60% of the cases investigated here, ConsDock was able to rank as top solution a pose within 2 Å RMSD of the X‐ray structure. It can be applied as a postprocessing filter to either single‐ or multiple‐docking programs to prioritize three‐dimensional guided lead optimization from the most likely docking solution. Proteins 2002;47:521–533. © 2002 Wiley‐Liss, Inc. Show less