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Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Molecular docking is a computational chemistry method which has become essential for the rational drug design process. In this context, it has had great impact as a successful tool for the study of ligand–receptor interaction modes, and for the exploration of large chemical datasets through virtual screening experiments. Despite their unquestionable merits, docking methods are not reliable for predicting binding energies due to the simple scoring functions they use. However, comparisons between two or three complexes using the predicted binding energies as a criterion are commonly found in the literature. In the present work we tested how wise is it to trust the docking energies when two complexes between a target protein and enantiomer pairs are compared. For this purpose, a ligand library composed by 141 enantiomeric pairs was used, including compounds with biological activities reported against seven protein targets. Docking results using the software Glide (considering extra precision (XP), standard precision (SP), and high-throughput virtual screening (HTVS) modes) and AutoDock Vina were compared with the reported biological activities using a classification scheme. Our test failed for all modes and targets, demonstrating that an accurate prediction when binding energies of enantiomers are compared using docking may be due to chance. We also compared pairs of compounds with different molecular weights and found the same results.

Molecular docking is the most frequently used computational method for studying the interactions between organic molecules and biological macromolecules. In this context, docking allows predicting the preferred pose of a ligand inside a receptor binding site. However, the selection of the “best” solution is not a trivial task, despite the widely accepted selection criterion that the best pose corresponds to the best energy score. Here, several rigid-target docking methods were evaluated on the same dataset with respect to their ability to reproduce crystallographic binding orientations, to test if the best energy score is a reliable criterion for selecting the best solution. For this, two experiments were performed: (A) to reconstruct the ligand-receptor complex by performing docking of the ligand in its own crystal structure receptor (defined as self-docking), and (B) to reconstruct the ligand-receptor complex by performing docking of the ligand in a crystal structure receptor that contains other ligand (defined as cross-docking). Root-mean square deviation (RMSD) was used to evaluate how different the obtained docking orientation is from the corresponding co-crystallized pose of the same ligand molecule. We found that docking score function is capable of predicting crystallographic binding orientations, but the best ranked solution according to the docking energy is not always the pose that reproduces the experimental binding orientation. This happened when self-docking was achieved, but it was critical in cross-docking. Taking into account that docking is typically used with predictive purposes, during cross-docking experiments, our results indicate that the best energy score is not a reliable criterion to select the best solution in common docking applications. It is strongly recommended to choose the best docking solution according to the scoring function along with additional structural criteria described for analogue ligands to assure the selection of a correct docking solution.

With the recent explosion of chemical libraries beyond a billion molecules, more efficient virtual screening approaches are needed. The Deep Docking (DD) platform enables up to 100-fold acceleration of structure-based virtual screening by docking only a subset of a chemical library, iteratively synchronized with a ligand-based prediction of the remaining docking scores. This method results in hundreds- to thousands-fold virtual hit enrichment (without significant loss of potential drug candidates) and hence enables the screening of billion molecule–sized chemical libraries without using extraordinary computational resources. Herein, we present and discuss the generalized DD protocol that has been proven successful in various computer-aided drug discovery (CADD) campaigns and can be applied in conjunction with any conventional docking program. The protocol encompasses eight consecutive stages: molecular library preparation, receptor preparation, random sampling of a library, ligand preparation, molecular docking, model training, model inference and the residual docking. The standard DD workflow enables iterative application of stages 3–7 with continuous augmentation of the training set, and the number of such iterations can be adjusted by the user. A predefined recall value allows for control of the percentage of top-scoring molecules that are retained by DD and can be adjusted to control the library size reduction. The procedure takes 1–2 weeks (depending on the available resources) and can be completely automated on computing clusters managed by job schedulers. This open-source protocol, at https://github.com/jamesgleave/DD_protocol, can be readily deployed by CADD researchers and can significantly accelerate the effective exploration of ultra-large portions of a chemical space.Screening chemical databases by computational docking is prohibitively time consuming when the databases are very large. Deep docking is a deep-learning approach aimed at reducing the number of compounds that need to be docked.

The chemistry and biochemistry of carbohydrate esters are essential parts of biochemical and medicinal research. A group of methyl β-d-galactopyranoside (β-MGP, 1) derivatives was acylated with 3-bromobenzoyl chloride and 4-bromobenzoyl chloride in anhydrous N,N-dimethylformamide/triethylamine to obtain 6-O-substitution products, which were subsequently converted into 2,3,4-tri-O-acyl derivatives with different aliphatic and aromatic substituents. Spectroscopic and elemental data exploration of these derivatives confirmed their chemical structures. In vitro biological experiments against five bacteria and two fungi and the prediction of activity spectra for substances (PASS) revealed ascending antifungal and antibacterial activities compared with their antiviral activities. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) experiments were performed for two derivatives, 3 and 9, based on their antibacterial activities. Most of these derivatives showed >780% inhibition of fungal mycelial growth. Density functional theory (DFT) was used to calculate the chemical descriptors and thermodynamic properties, whereas molecular docking was performed against antibacterial drug targets, including PDB: 4QDI, 5A5E, 7D27, 1ZJI, 3K8E, and 2MRW, and antifungal drug targets, such as PDB: 1EA1 and 1AI9, to identify potential drug candidates for microbial pathogens. A 100 ns molecular dynamics simulation study revealed stable conformation and binding patterns in a stimulating environment by their uniform RMSD, RMSF, SASA, H-bond, and RoG profiles. In silico pharmacokinetic and quantitative structure–activity relationship (QSAR) calculations (pIC50 values 3.67~8.15) suggested that all the designed β-MGP derivatives exhibited promising results due to their improved kinetic properties with low aquatic and non-aquatic toxicities. These biological, structure–activity relationship (SAR) [lauroyl-(CH3(CH2)10CO-) group was found to have potential], and in silico computational studies revealed that the newly synthesized MGP derivatives are potential antibacterial/antifungal candidates and can serve as therapeutic targets for human and plant pathogens.

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein‐ligand interactions between 296 proteins spanning Escherichia coli 's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning‐based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true‐positive rate to false‐positive rate. This work indicates that advances in modeling protein‐ligand interactions, particularly using machine learning‐based approaches, are needed to better harness AlphaFold2 for drug discovery. Synopsis Assessing molecular docking simulations based on AlphaFold2‐predicted structures with high‐throughput measurements of protein‐ligand interactions reveals weak model performance. Machine learning‐based approaches improve performance and better harness AlphaFold2 for drug discovery. AlphaFold2‐based molecular docking predictions for 296 Escherichia coli proteins, 218 active antibacterial compounds and 100 inactive compounds predict widespread promiscuity and similar distributions of binding affinities between active and inactive compounds. Quantitative enzymatic inhibition assays for 12 essential E. coli proteins treated with each of the 218 antibacterial compounds confirm extensive promiscuity. The enzymatic inhibition dataset reveals that the performance of the molecular docking model is weak. Rescoring of docking poses using machine learning‐based scoring functions improves model performance. Graphical Abstract Assessing molecular docking simulations based on AlphaFold2‐predicted structures with high‐throughput measurements of protein‐ligand interactions reveals weak model performance. Machine learning‐based approaches improve performance and better harness AlphaFold2 for drug discovery.

The current emergency due to the worldwide spread of the COVID-19 caused by the new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a great concern for global public health. Already in the past, the outbreak of severe acute respiratory syndrome (SARS) in 2003 and Middle Eastern respiratory syndrome (MERS) in 2012 demonstrates the potential of coronaviruses to cross-species borders and further underlines the importance of identifying new-targeted drugs. An ideal antiviral agent should target essential proteins involved in the lifecycle of SARS-CoV. Currently, some HIV protease inhibitors (i.e., Lopinavir) are proposed for the treatment of COVID-19, although their effectiveness has not yet been assessed. The main protease (Mpro) provides a highly validated pharmacological target for the discovery and design of inhibitors. We identified potent Mpro inhibitors employing computational techniques that entail the screening of a Marine Natural Product (MNP) library. MNP library was screened by a hyphenated pharmacophore model, and molecular docking approaches. Molecular dynamics and re-docking further confirmed the results obtained by structure-based techniques and allowed this study to highlight some crucial aspects. Seventeen potential SARS-CoV-2 Mpro inhibitors have been identified among the natural substances of marine origin. As these compounds were extensively validated by a consensus approach and by molecular dynamics, the likelihood that at least one of these compounds could be bioactive is excellent.

On average, an approved drug currently costs US$2–3 billion and takes more than 10 years to develop 1 . In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened 2 . However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant ( K d ) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins. VirtualFlow, an open-source drug discovery platform, enables the efficient preparation and virtual screening of ultra-large ligand libraries to identify molecules that bind with high affinity to target proteins.

Alzheimer’s disease is a major public brain condition that has resulted in many deaths, as revealed by the World Health Organization (WHO). Conventional Alzheimer’s treatments such as chemotherapy, surgery, and radiotherapy are not very effective and are usually associated with several adverse effects. Therefore, it is necessary to find a new therapeutic approach that completely treats Alzheimer’s disease without many side effects. In this research project, we report the synthesis and biological activities of some new thiazole-bearing sulfonamide analogs (1–21) as potent anti-Alzheimer’s agents. Suitable characterization techniques were employed, and the density functional theory (DFT) computational approach, as well as in-silico molecular modeling, has been employed to assess the electronic properties and anti-Alzheimer’s potency of the analogs. All analogs exhibited a varied degree of inhibitory potential, but analog 1 was found to have excellent potency (IC50 = 0.10 ± 0.05 µM for AChE) and (IC50 = 0.20 ± 0.050 µM for BuChE) as compared to the reference drug donepezil (IC50 = 2.16 ± 0.12 µM and 4.5 ± 0.11 µM). The structure-activity relationship was established, and it mainly depends upon the nature, position, number, and electron-donating/-withdrawing effects of the substituent/s on the phenyl rings.

Huanglian Jiedu Decoction (HLJDD) is a classical herbal formula with potential efficacy in the treatment of sepsis. However, the main components and potential mechanisms of HLJDD remain unclear. This study aims to initially clarify the potential mechanism of HLJDD in the treatment of sepsis based on network pharmacology and molecular docking techniques. The principal components and corresponding protein targets of HLJDD were searched on TCMSP, BATMAN-TCM and ETCM and the compound-target network was constructed by Cytoscape3.8.2. Sepsis targets were searched on OMIM and DisGeNET databases. The intersection of compound target and disease target was obtained and the coincidence target was imported into STRING database to construct a PPI network. We further performed GO and KEGG enrichment analysis on the targets. Finally, molecular docking study was approved for the core target and the active compound. There are 257 nodes and 792 edges in the component target network. The compounds with a higher degree value are quercetin, kaempferol, and wogonin. The protein with a higher degree in the PPI network is JUN, RELA, TNF. GO and KEGG analysis showed that HLJDD treatment of sepsis mainly involves positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptosis process, response to hypoxia and other biological processes. The signaling pathways mainly include PI3K-AKT, MAPK, TNF signaling pathway. The molecular docking results showed that quercetin, kaempferol and wogonin have higher affinity with JUN, RELA and TNF. This study reveals the active ingredients and potential molecular mechanism of HLJDD in the treatment of sepsis, and provides a reference for subsequent basic research. •Network pharmacology was utilized to determine the mechanisms and targets of HLJDD.•Molecular docking elucidated three key anti-sepsis components in HLJDD.•The combining of molecular docking and network pharmacology provides a way for the study of traditional Chinese medicine.