Can we use docking and scoring for hit-to-lead optimization?

Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products

Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.

Discovery of Novel Rhizoctonia solani DHFR Inhibitors as Fungicides Using Virtual Screening

Dihydrofolate reductase (DHFR) is an essential enzyme in the folate pathway and has been recognized as a well-known target for antibacterial and antifungal drugs. We discovered eight compounds from the ZINC database using virtual screening to inhibit Rhizoctonia solani (R. solani), a fungal pathogen in crops. These compounds were evaluated with in vitro assays for enzymatic and antifungal activity. Among these, compound Hit8 is the most active R. solani DHFR inhibitor, with the IC50 of 10.2 μM. The selectivity of inhibition is 22.3 against human DHFR with the IC50 of 227.7 μM. Moreover, Hit8 has higher antifungal activity against R. solani (EC50 of 38.2 mg L-1) compared with validamycin A (EC50 of 67.6 mg L-1), a well-documented fungicide. These results suggest that Hit8 may be a potential fungicide. Our study exemplifies a computer-aided method to discover novel inhibitors that could target plant pathogenic fungi.

In silico and in vivo anti-angiogenic validation on ethanolic extract of Curcuma longa and curcumin compound in hepatocellular carcinoma through mitogen activated protein kinase expression in male and female wistar rats

Hepatocellular carcinoma (HCC) is the most frequent primary malignancy of the liver. The aim of this study is to evaluate the comparative in silico and in vivo ameliorative potential of the ethanolic extract of Curcuma longa (EECL) in male and female Wistar rats administered N-nitrosodiethylamine-induced hepatocellular carcinoma. The MAPK compound was obtained from a protein data bank (PDB ID: 7AUV) for molecular docking. One hundred and twenty Wistar rats, were randomly selected into twelve groups (n = 5): Group A received regular diets as a basal control; groups B to G were administered 100 mg/kg NDEA twice in two weeks; while groups C to E received 200 mg/kg, 400 mg/kg, and 600 mg/kg of EECL; group F was treated with 200 mg/kg pure curcumin; and group G received 100 mg/kg Sylibon-140. Group H received only 200 mg/kg pure curcumin, and group I received 200 mg/kg of dimethylsulfoxide (DMSO). Groups J, K, and L received 200 mg/kg, 400 mg/kg and 600 mg/kg of EECL. MAPK and AFP mRNA in Wistar rats administered NDEA were upregulated as compared to EECL groups. In conclusion, the in silico and in vitro study validates the mitigating role of ethanolic extract of Curcuma longa and pure curcumin.

Integrating structure-based approaches in generative molecular design

Generative molecular design for drug discovery and development has seen a recent resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by computationally exploring much larger chemical spaces than traditional virtual screening techniques. However, most generative models thus far have only utilized small-molecule information to train and condition de novo molecule generators. Here, we instead focus on recent approaches that incorporate protein structure into de novo molecule optimization in an attempt to maximize the predicted on-target binding affinity of generated molecules. We summarize these structure integration principles into either distribution learning or goal-directed optimization and for each case whether the approach is protein structure-explicit or implicit with respect to the generative model. We discuss recent approaches in the context of this categorization and provide our perspective on the future direction of the field.

Application of Computational Biology and Artificial Intelligence in Drug Design

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

Present and future challenges in therapeutic designing using computational approaches

Currently, various computational methods are being used for the purpose of therapeutic design. The advent of the Coronavirus disease-2019 (COVID-19) pandemic has created a lot of problems due to which the development of effective treatment options is urgently needed. Computational intelligence is used in the control, prevention, prediction, diagnosis, and treatment of the disease. Several important drug targets have been identified in severe acute respiratory syndrome-Coronavirus-2 using in silico methods. Computer-aided drug design includes a variety of theoretical and computational approaches that are part of modern drug discovery. Advances in machine learning methods and their applications speed up the drug discovery process. Exploration of nucleic acid-based therapeutics is playing an important role in healthcare also. But a lot of challenges have also been seen that complicate the therapeutic design. Therefore, investigation of challenges associated with therapeutic design is important, and the present chapter is aimed to cover various therapeutic design approaches and challenges associated with them. Moreover, the role of computational strategies in the exploration of potential therapeutics against COVID-19 has been investigated.

Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges

Artificial intelligence (AI) has undergone rapid development in recent years and has been successfully applied to real-world problems such as drug design. In this chapter, we review recent applications of AI to problems in drug design including virtual screening, computer-aided synthesis planning, and de novo molecule generation, with a focus on the limitations of the application of AI therein and opportunities for improvement. Furthermore, we discuss the broader challenges imposed by AI in translating theoretical practice to real-world drug design; including quantifying prediction uncertainty and explaining model behavior.

Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it.

Identification of novel allosteric binding sites and multi-targeted allosteric inhibitors of receptor and non-receptor tyrosine kinases using a computational approach

EGFR1, VEGFR2, Bcr-Abl and Src kinases are key drug targets in non-small cell lung cancer (NSCLC), bladder cancer, pancreatic cancer, CML, ALL, colorectal cancer, etc. The available drugs targeting these kinases have limited therapeutic efficacy due to novel mutations resulting in drug resistance and toxicity, as they target ATP binding site. Allosteric drugs have shown promising results in overcoming drug resistance, but the discovery of allosteric drugs is challenging. The allosteric binding pockets are difficult to predict, as they are generally associated with high energy conformations and regulate protein function in yet unknown mechanisms. In addition, the discovery of drugs using conventional methods takes long time and goes through several challenges, putting the lives of many cancer patients at risk. Therefore, the aim of the present work was to apply the most successful, drug repurposing approach in combination with computational methods to identify kinase inhibitors targeting novel allosteric sites on protein structure and assess their potential multi-kinase binding affinity. Multiple crystal structures belonging to EGFR1, VEGFR2, Bcr-Abl and Src tyrosine kinases were selected, including mutated, inhibitor bound and allosteric conformations to identify potential leads, close to physiological conditions. Interestingly the potential inhibitors identified were peptides. The drugs identified in this study could be used in therapy as a single multi-kinase inhibitor or in a combination of single kinase inhibitors after experimental validation. In addition, we have also identified new hot spots that are likely to be druggable allosteric sites for drug discovery of kinase-specific drugs in the future.Communicated by Ramaswamy H. Sarma.

Structure-based molecular modeling in SAR analysis and lead optimization

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

VEGFR-2 kinase domain inhibition as a scaffold for anti-angiogenesis: Validation of the anti-angiogenic effects of carotenoids from Spondias mombin in DMBA model of breast carcinoma in Wistar rats

Vascular endothelial growth factor (VEGF) and its receptor-2 (VEGFR-2) mediated tumorigenesis, metastasis, and angiogenesis are the cause of the increased levels of mortality associated with breast cancer and other forms of cancer. Inhibition of VEGF and VEGFR-2 provides a great therapeutic option in the management of cancer. This study employed VEGFR-2 kinase domain inhibition as an anti-angiogenic scaffold and further validate the anti-angiogenic effects of the lead phytochemicals, carotenoids from Spondias mombin in 7, 12-Dimethylbenz[a]anthracene (DMBA) model of breast carcinoma in Wistar rats. Phytochemicals characterized from 6 reported anti-cancer plants were screened against the VEGFR-2 kinase domain. The lead phytochemicals, carotenoids from Spondias mombin were isolated and subjected to Liquid Chromatography-Electrospray Ionization-Mass Spectrometry (LC-ESI-MS) for characterization. The anti-angiogenic potentials of the carotenoid isolates were validated in the DMBA model of breast carcinoma in female Wistar rats through assessment of the expression of anti-angiogenic related mRNAs, histopathological analysis, and molecular docking. Treatment with carotenoid isolates (100 mg/kg and 200 mg/kg) significantly (p < 0.05) downregulated the expression of VEGF, VEGFR, Epidermal Growth Factor Receptor (EGFR), Hypoxia-Inducible Factor-1(HIF-1), and Matrix Metalloproteinase-2 (MMP-2) mRNAs in the mammary tumours, while the expression of Chromodomain Helicase DNA-Binding Protein-1 (CHD-1) mRNA was significantly (p < 0.05) upregulated. DMBA induced comedo and invasive ductal subtypes of breast carcinoma. The binding of astaxanthin, 7,7',8,8'-tetrahydro-β,β-carotene, and beta-carotene-15,15'-epoxide to the ATP binding site led to the DFG-out conformation with binding energies of -8.2 kcal/mol, -10.3 kcal/mol, and -10.5 kcal/mol respectively. Carotenoid isolates demonstrated anti-angiogenic and anti-proliferating potentials via VEGFR-2 kinase domain inhibition.

Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery

Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.

Synthesis, In Silico and In Vitro Evaluation of Some Flavone Derivatives for Acetylcholinesterase and BACE-1 Inhibitory Activity

Acetylcholinesterase (AChE) and β-secretase (BACE-1) have become attractive therapeutic targets for Alzheimer’s disease (AD). Flavones are flavonoid derivatives with various bioactive effects, including AChE and BACE-1 inhibition. In the present work, a series of 14 flavone derivatives was synthesized in relatively high yields (35–85%). Six of the synthetic flavones (B4, B5, B6, B8, D6 and D7) had completely new structures. The AChE and BACE-1 inhibitory activities were tested, giving pIC₅₀ 3.47–4.59 (AChE) and 4.15–5.80 (BACE-1). Three compounds (B3, D5 and D6) exhibited the highest biological effects on both AChE and BACE-1. A molecular docking investigation was conducted to explain the experimental results. These molecules could be employed for further studies to discover new structures with dual action on both AChE and BACE-1 that could serve as novel therapies for AD.

Auto In Silico Ligand Directing Evolution to Facilitate the Rapid and Efficient Discovery of Drug Lead

Motivated by the growing demand for reducing the chemical optimization burden of H2L, we developed auto in silico ligand directing evolution (AILDE, http://chemyang.ccnu.edu.cn/ccb/server/AILDE), an efficient and general approach for the rapid identification of drug leads in accessible chemical space. This computational strategy relies on minor chemical modifications on the scaffold of a hit compound, and it is primarily intended for identifying new lead compounds with minimal losses or, in some cases, even increases in ligand efficiency. We also described how AILDE greatly reduces the chemical optimization burden in the design of mesenchymal-epithelial transition factor (c-Met) kinase inhibitors. We only synthesized eight compounds and found highly efficient compound 5g, which showed an ∼1,000-fold improvement in in vitro activity compared with the hit compound. 5g also displayed excellent in vivo antitumor efficacy as a drug lead. We believe that AILDE may be applied to a large number of studies for rapid design and identification of drug leads.

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AILDE was developed for the rapid identification of drug leads

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A potent drug lead targeted to c-Met was found by synthesizing only eight compounds

Structure- and Ligand-Based Virtual Screening on DUD-E+: Performance Dependence on Approximations to the Binding Pocket

Using the DUD-E⁺ benchmark, we explore the impact of using a single protein pocket or ligand for virtual screening compared with using ensembles of alternative pockets, ligands, and sets thereof. For both structure-based and ligand-based approaches, the precise characterization of the binding site in question had a significant impact on screening performance. Using the single original DUD-E protein, Surflex-Dock yielded mean ROC area of 0.81 ± 0.11. Using the cognate ligand instead, with the eSim method for screening, yielded 0.77 ± 0.14. Moving to ensembles of five protein pocket variants increased docking performance to 0.84 ± 0.09. Results for the analogous ligand-based approach (using the five crystallographically aligned cognate ligands) was 0.83 ± 0.11. Using the same ligands, but making use of an automatically generated mutual alignment, yielded mean AUC nearly as good as from single-structure docking: 0.80 ± 0.12. Detailed results and statistical analyses show that structure- and ligand-based methods are complementary and can be fruitfully combined to enhance screening efficiency. A hybrid approach combining ensemble docking with eSim-based screening produced the best and most consistent performance (mean ROC area of 0.89 ± 0.08 and 1% early enrichment of 46-fold). Based on results from both the docking and ligand-similarity approaches, it is clearly unwise to make use of a single arbitrarily chosen protein structure for docking or single ligand query for similarity-based screening.

High-Throughput Docking Using Quantum Mechanical Scoring

Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets.

Identification of potent L,D-transpeptidase 5 inhibitors for Mycobacterium tuberculosis as potential anti-TB leads: virtual screening and molecular dynamics simulations

Virtual screening is a useful in silico approach to identify potential leads against various targets. It is known that carbapenems (doripenem and faropenem) do not show any reasonable inhibitory activities against L,D-transpeptidase 5 (Ldt_Mt5) and also an adduct of meropenem exhibited slow acylation. Since these drugs are active against L,D-transpeptidase 2 (Ldt_Mt2), understanding the differences between these two enzymes is essential. In this study, a ligand-based virtual screening of 12,766 compounds followed by molecular dynamics (MD) simulations was applied to identify potential leads against Ldt_Mt5. To further validate the obtained virtual screening ranking for Ldt_Mt5, we screened the same libraries of compounds against Ldt_Mt2 which had more experimetal and calculated binding energies reported. The observed consistency between the binding affinities of Ldt_Mt2 validates the obtained virtual screening binding scores for Ldt_Mt5. We subjected 37 compounds with docking scores ranging from - 7.2 to - 9.9 kcal mol^-1 obtained from virtual screening for further MD analysis. A set of compounds (n = 12) from four antibiotic classes with ≤ - 30 kcal mol^-1 molecular mechanics/generalized born surface area (MM-GBSA) binding free energies (ΔG_bind) was characterized. A final set of that, all β-lactams (n = 4), was considered. The outcome of this study provides insight into the design of potential novel leads for Ldt_Mt5. Graphical abstract.

Electrostatic-field and surface-shape similarity for virtual screening and pose prediction

We introduce a new method for rapid computation of 3D molecular similarity that combines electrostatic field comparison with comparison of molecular surface-shape and directional hydrogen-bonding preferences (called "eSim"). Rather than employing heuristic "colors" or user-defined molecular feature types to represent conformation-dependent molecular electrostatics, eSim calculates the similarity of the electrostatic fields of two molecules (in addition to shape and hydrogen-bonding). We present detailed virtual screening performance data on the standard 102 target DUD-E set. In its moderately fast screening mode, eSim running on a single computing core is capable of processing over 60 molecules per second. In this mode, eSim performed significantly better than all alternate methods for which full DUD-E data were available (mean ROC area of 0.74, p [Formula: see text], by paired t-test, compared with the best performing alternate method). In addition, for 92 targets of the DUD-E set where multiple ligand-bound crystal structures were available, screening performance was assessed using alternate ligands or sets thereof (in their bound poses) as similarity targets. Using the joint alignment of five ligands for each protein target, mean ROC area exceeded 0.82 for the 92 targets. Design-focused application of ligand similarity methods depends on accurate predictions of geometric molecular relationships. We comprehensively assessed pose prediction accuracy by curating nearly 400,000 bound ligand pose pairs across the DUD-E targets. Overall, beginning from agnostic initial poses, we observed an 80% success rate for RMSD [Formula: see text] Å  among the top 20 predicted eSim poses. These examples were split roughly 50/50 into cases with high direct atomic overlap (where a shared scaffold exists between a pair) and low direct atomic overlap (where where a ligand pair has dissimilar scaffolds but largely occupies the same space). Within the high direct atomic overlap subset, the pose prediction success rate was 93%. For the more challenging subset (where dissimilar scaffolds are to be aligned), the success rate was 70%. The eSim approach enables both large-scale screening and rational design of ligands and is rooted in physically meaningful, non-heuristic, molecular comparisons.

Comparative Assessment of Seven Docking Programs on a Nonredundant Metalloprotein Subset of the PDBbind Refined

Extensive usage of molecular docking for computer-aided drug discovery resulted in development of numerous programs with versatile scoring and posing algorithms. Selection of the docking program among these vast number of options is central to the outcome of drug discovery. To this end, comparative assessment studies of docking offer valuable insights into the selection of the optimal tool. Despite the availability of various docking assessment studies, the performance difference of docking programs has not been well addressed on metalloproteins which comprise a substantial portion of the human proteome and have been increasingly targeted for treatment of a wide variety of diseases. This study reports comparative assessment of seven docking programs on a diverse metalloprotein set which was compiled for this study. The refined set of the PDBbind (2017) was screened to gather 710 complexes with metal ion(s) closely located to the ligands (<4 Å). The redundancy was eliminated by clustering and overall 213 complexes were compiled as the nonredundant metalloprotein subset of the PDBbind refined. The scoring, ranking, and posing powers of seven noncommercial docking programs, namely, AutoDock4, AutoDock4_Zn, AutoDock Vina, Quick Vina 2, LeDock, PLANTS, and UCSF DOCK6, were comprehensively evaluated on this nonredundant set. Results indicated that PLANTS (80%) followed by LeDock (77%), QVina (76%), and Vina (73%) had the most accurate posing algorithms while AutoDock4 (48%) and DOCK6 (56%) were the least successful in posing. Contrary to their moderate-to-high level of posing success, none of the programs was successful in scoring or ranking of the binding affinities (r² ≈ 0). Screening power was further evaluated by using active-decoy ligand sets for a large compilation of metalloprotein targets. PLANTS stood out among other programs to be able to enrich the active ligand for every target, underscoring its robustness for screening of metalloprotein inhibitors. This study provides useful information for drug discovery studies targeting metalloproteins.

Understanding Aldose Reductase-Inhibitors interactions with free energy simulation

Aldose Reductase (AR) reduces a variety of substrates, such as aldehydes, aldoses and corticosteroids. It is the first and rate-limiting enzyme of the polyol pathway and is an important target enzyme for diabetes-associated complications, including retinopathy, neuropathy, and nephropathy. Inhibitors targeting this enzyme are structurally different and some of them have side effects. In existing publications, computational techniques are applied to investigate the binding affinities of existing inhibitors and predicting the affinities of newly designed ligands. However, these calculations only employ coarse and approximated methods such as docking and MM/PBSA. Brute force simulations are employed to study the dynamics of the system but no converged statistics is obtained. As a result, these computations provide results not consistent with experimental values and large discrepancies exist. In the current work, we employ the enhanced sampling technique of alchemical free energy simulation to calculate the binding affinities of several ligands targeting AR. The statistical error is defined with care and the correlation in the time-series data is fully considered. The statistically optimal estimators are used to extract the free energy estimates and the predicted results are in agreement with the experimental values. Less computationally demanding end-point free energy methods are also performed to compare their efficiency with the alchemical methods. As is expected, the end-point methods are of less accuracy and reliability compared with the alchemical free energy methods. The decomposition of the free energy difference in each alchemical transformation into the enthalpic and entropic components gives further insights on the thermodynamics. The enthalpy-entropy compensation is observed in this case. As the structural data obtained from experiments are only snapshots and more details are needed to understand the dynamics of the protein-ligand system, the conformational ensemble is analyzed. We identify important residues involved in the protein-ligand binding case and short-lived interactions formed due to fluctuations in the conformational ensemble. The current work shed light on the atomic detailed understanding of the dynamics of AR-inhibitors interactions.

Fentanyl Family at the Mu-Opioid Receptor: Uniform Assessment of Binding and Computational Analysis

Interactions of 21 fentanyl derivatives with μ-opioid receptor (μOR) were studied using experimental and theoretical methods. Their binding to μOR was assessed with radioligand competitive binding assay. A uniform set of binding affinity data contains values for two novel and one previously uncharacterized derivative. The data confirms trends known so far and thanks to their uniformity, they facilitate further comparisons. In order to provide structural hypotheses explaining the experimental affinities, the complexes of the studied derivatives with μOR were modeled and subject to molecular dynamics simulations. Five common General Features (GFs) of fentanyls' binding modes stemmed from these simulations. They include: GF1) the ionic interaction between D147 and the ligands' piperidine NH⁺ moiety; GF2) the N-chain orientation towards the μOR interior; GF3) the other pole of ligands is directed towards the receptor outlet; GF4) the aromatic anilide ring penetrates the subpocket formed by TM3, TM4, ECL1 and ECL2; GF5) the 4-axial substituent (if present) is directed towards W318. Except for the ionic interaction with D147, the majority of fentanyl-μOR contacts is hydrophobic. Interestingly, it was possible to find nonlinear relationships between the binding affinity and the volume of the N-chain and/or anilide's aromatic ring. This kind of relationships is consistent with the apolar character of interactions involved in ligand⁻receptor binding. The affinity reaches the optimum for medium size while it decreases for both large and small substituents. Additionally, a linear correlation between the volumes and the average dihedral angles of W293 and W133 was revealed by the molecular dynamics study. This seems particularly important, as the W293 residue is involved in the activation processes. Further, the Y326 (OH) and D147 (Cγ) distance found in the simulations also depends on the ligands' size. In contrast, neither RMSF measures nor D114/Y336 hydrations show significant structure-based correlations. They also do not differentiate studied fentanyl derivatives. Eventually, none of 14 popular scoring functions yielded a significant correlation between the predicted and observed affinity data (R < 0.30, n = 28).

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

An Efficient Implementation of the Nwat-MMGBSA Method to Rescore Docking Results in Medium-Throughput Virtual Screenings

Nwat-MMGBSA is a variant of MM-PB/GBSA based on the inclusion of a number of explicit water molecules that are the closest to the ligand in each frame of a molecular dynamics trajectory. This method demonstrated improved correlations between calculated and experimental binding energies in both protein-protein interactions and ligand-receptor complexes, in comparison to the standard MM-GBSA. A protocol optimization, aimed to maximize efficacy and efficiency, is discussed here considering penicillopepsin, HIV1-protease, and BCL-XL as test cases. Calculations were performed in triplicates on both classic HPC environments and on standard workstations equipped by a GPU card, evidencing no statistical differences in the results. No relevant differences in correlation to experiments were also observed when performing Nwat-MMGBSA calculations on 4 or 1 ns long trajectories. A fully automatic workflow for structure-based virtual screening, performing from library set-up to docking and Nwat-MMGBSA rescoring, has then been developed. The protocol has been tested against no rescoring or standard MM-GBSA rescoring within a retrospective virtual screening of inhibitors of AmpC β-lactamase and of the Rac1-Tiam1 protein-protein interaction. In both cases, Nwat-MMGBSA rescoring provided a statistically significant increase in the ROC AUCs of between 20 and 30%, compared to docking scoring or to standard MM-GBSA rescoring.

Quantum mechanics implementation in drug-design workflows: does it really help?

The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry. However, the complexity of many protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. The usefulness of quantum mechanics (QM) in drug-protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules. We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry?

Tinker-OpenMM: Absolute and relative alchemical free energies using AMOEBA on GPUs

The capabilities of the polarizable force fields for alchemical free energy calculations have been limited by the high computational cost and complexity of the underlying potential energy functions. In this work, we present a GPU-based general alchemical free energy simulation platform for polarizable potential AMOEBA. Tinker-OpenMM, the OpenMM implementation of the AMOEBA simulation engine has been modified to enable both absolute and relative alchemical simulations on GPUs, which leads to a ∼200-fold improvement in simulation speed over a single CPU core. We show that free energy values calculated using this platform agree with the results of Tinker simulations for the hydration of organic compounds and binding of host-guest systems within the statistical errors. In addition to absolute binding, we designed a relative alchemical approach for computing relative binding affinities of ligands to the same host, where a special path was applied to avoid numerical instability due to polarization between the different ligands that bind to the same site. This scheme is general and does not require ligands to have similar scaffolds. We show that relative hydration and binding free energy calculated using this approach match those computed from the absolute free energy approach. © 2017 Wiley Periodicals, Inc.

Biobetters From an Integrated Computational/Experimental Approach

Biobetters are new drugs designed from existing peptide or protein-based therapeutics by improving their properties such as affinity and selectivity for the target epitope, and stability against degradation. Computational methods can play a key role in such design problems—by predicting the changes that are most likely to succeed, they can drastically reduce the number of experiments to be performed. Here we discuss the computational and experimental methods commonly used in drug design problems, focusing on the inverse relationship between the two, namely, the more accurate the computational predictions means the less experimental effort is needed for testing. Examples discussed include efforts to design selective analogs from toxin peptides targeting ion channels for treatment of autoimmune diseases and monoclonal antibodies which are the fastest growing class of therapeutic agents particularly for cancers and autoimmune diseases.

Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors

Nonstructural 5B (NS5B) polymerase and Nonstructural 3/4A (NS3/4A) protease have proven to be promising targets for the development of anti-HCV (Hepatitis C Virus) agents. The NS5B polymerase is of paramount importance in HCV viral replication; therefore, employing NS5B inhibitors was considered an effective way for the treatment of HCV. Identifying inhibitors against NS3/4A serine protease represents another attractive approach applied in anti-HCV drug discovery, which is evidenced by its crucial role of in the biogenesis of the viral replication activity. In this chapter, many different computational approaches including Quantitative Structure-Activity Relationship (QSAR) and virtual screening in anti-HCV drug discovery were considered and discussed in detail. Virtual Screening (VS) techniques, including ligand-based and structure-based, and QSAR have been utilized for the discovery of NS5B inhibitors. Moreover, using various in silico protocols and workflows, a number of studies have been conducted with an aim of identifying potential NS3/4A blockage agents.

Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Empirical Scoring Functions for Affinity Prediction of Protein-ligand Complexes

The ability to rapidly assess the quality of a protein-ligand complex in terms of its affinity is of fundamental importance for various methods of computer-aided drug design. While simple filtering or matching critieria may be sufficient in fast docking methods or at early stages of virtual screening, estimates of the actual free energy of binding are needed whenever refined docking solutions, ligand rankings or support for the optimization of hit compounds are required. If rigorous free energy calculations based on molecular simulations are impractical, such affinity estimates are provided by scoring functions. The class of empirical scoring functions aims to provide them via a regression-based approach. Using experimental structures and affinity data of protein-ligand complexes and descriptors suitable to capture the essential features of the interaction, these functions are trained with classical linear regression techniques or machine-learning methods. The latter have led to considerable improvements in terms of prediction accuracy for large generic data sets. Nevertheless, many limitations are not yet resolved and pose significant challenges for future developments.

Effective in silico prediction of new oxazolidinone antibiotics: force field simulations of the antibiotic-ribosome complex supervised by experiment and electronic structure methods

We propose several new and promising antibacterial agents for the treatment of serious Gram-positive infections. Our predictions rely on force field simulations, supervised by first principle calculations and available experimental data. Different force fields were tested in order to reproduce linezolid's conformational space in terms of a) the isolated and b) the ribosomal bound state. In a first step, an all-atom model of the bacterial ribosome consisting of nearly 1600 atoms was constructed and evaluated. The conformational space of 30 different ribosomal/oxazolidinone complexes was scanned by stochastic methods, followed by an evaluation of their enthalpic penalties or rewards and the mechanical strengths of the relevant hydrogen bonds (relaxed force constants; compliance constants). The protocol was able to reproduce the experimentally known enantioselectivity favoring the S-enantiomer. In a second step, the experimentally known MIC values of eight linezolid analogues were used in order to crosscheck the robustness of our model. In a final step, this benchmarking led to the prediction of several new and promising lead compounds. Synthesis and biological evaluation of the new compounds are on the way.

Computational approaches for designing potent and selective analogs of peptide toxins as novel therapeutics

Peptide toxins provide valuable therapeutic leads for many diseases. As they bind to their targets with high affinity, potency is usually ensured. However, toxins also bind to off-target receptors, causing potential side effects. Thus, a major challenge in generating drugs from peptide toxins is ensuring their specificity for their intended targets. Computational methods can play an important role in solving such design problems through construction of accurate models of receptor-toxin complexes and calculation of binding free energies. Here we review the computational methods used for this purpose and their application to toxins targeting ion channels. We describe ShK and HsTX1 toxins, high-affinity blockers of the voltage-gated potassium channel Kv1.3, which could be developed as therapeutic agents for autoimmune diseases.

In vitro, in silico and integrated strategies for the estimation of plasma protein binding. A review

Plasma protein binding (PPB) strongly affects drug distribution and pharmacokinetic behavior with consequences in overall pharmacological action. Extended plasma protein binding may be associated with drug safety issues and several adverse effects, like low clearance, low brain penetration, drug-drug interactions, loss of efficacy, while influencing the fate of enantiomers and diastereoisomers by stereoselective binding within the body. Therefore in holistic drug design approaches, where ADME(T) properties are considered in parallel with target affinity, considerable efforts are focused in early estimation of PPB mainly in regard to human serum albumin (HSA), which is the most abundant and most important plasma protein. The second critical serum protein α1-acid glycoprotein (AGP), although often underscored, plays also an important and complicated role in clinical therapy and thus the last years it has been studied thoroughly too. In the present review, after an overview of the principles of HSA and AGP binding as well as the structure topology of the proteins, the current trends and perspectives in the field of PPB predictions are presented and discussed considering both HSA and AGP binding. Since however for the latter protein systematic studies have started only the last years, the review focuses mainly to HSA. One part of the review highlights the challenge to develop rapid techniques for HSA and AGP binding simulation and their performance in assessment of PPB. The second part focuses on in silico approaches to predict HSA and AGP binding, analyzing and evaluating structure-based and ligand-based methods, as well as combination of both methods in the aim to exploit the different information and overcome the limitations of each individual approach. Ligand-based methods use the Quantitative Structure-Activity Relationships (QSAR) methodology to establish quantitate models for the prediction of binding constants from molecular descriptors, while they provide only indirect information on binding mechanism. Efforts for the establishment of global models, automated workflows and web-based platforms for PPB predictions are presented and discussed. Structure-based methods relying on the crystal structures of drug-protein complexes provide detailed information on the underlying mechanism but are usually restricted to specific compounds. They are useful to identify the specific binding site while they may be important in investigating drug-drug interactions, related to PPB. Moreover, chemometrics or structure-based modeling may be supported by experimental data a promising integrated alternative strategy for ADME(T) properties optimization. In the case of PPB the use of molecular modeling combined with bioanalytical techniques is frequently used for the investigation of AGP binding.

Calculation of free energy changes due to mutations from alchemical free energy simulations

How a mutation affects the binding free energy of a ligand is a fundamental problem in molecular biology/biochemistry with many applications in pharmacology and biotechnology, e.g. design of drugs and enzymes. Free energy change due to a mutation can be determined most accurately by performing alchemical free energy calculations in molecular dynamics (MD) simulations. Here we discuss the necessary conditions for success of free energy calculations using toxin peptides that bind to ion channels as examples. We show that preservation of the binding mode is an essential requirement but this condition is not always satisfied, especially when the mutation involves a charged residue. Otherwise problems with accuracy of results encountered in mutation of charged residues can be overcome by performing the mutation on the ligand in the binding site and bulk simultaneously and in the same system. The proposed method will be useful in improving the affinity and selectivity profiles of drug leads and enzymes via computational design and protein engineering.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

Synthesis and biological evaluation of direct thrombin inhibitors bearing 4-(piperidin-1-yl)pyridine at the P1 position with potent anticoagulant activity

The design and synthesis of a new class of nonpeptide direct thrombin inhibitors, built on the structure of 1-(pyridin-4-yl)piperidine-4-carboxamide, are described. Starting from a strongly basic 1-amidinopiperidine derivative (6) showing poor thrombin (fIIa) and factor Xa (fXa) inhibition activities, anti-fIIa activity and artificial membrane permeability were considerably improved by optimizing the basic P1 and the X-substituted phenyl P4 binding moieties. Structure-activity relationship studies, usefully complemented with molecular modeling results, led us to identify compound 13b, which showed excellent fIIa inhibition (Ki = 6 nM), weak anti-Xa activity (Ki = 5.64 μM), and remarkable selectivity over other serine proteases (e.g., trypsin). Compound 13b showed in vitro anticoagulant activity in the low micromolar range and significant membrane permeability. In mice (ex vivo), 13b demonstrated anticoagulant effects at 2 h after oral dosing (100 mg·kg(-1)), with a significant 43% prolongation of the activated partial thromboplastin time (aPTT), over controls (P < 0.05).

Structural characterization of the binding interactions of various endogenous estrogen metabolites with human estrogen receptor α and β subtypes: a molecular modeling study

In the present study, we used the molecular docking approach to study the binding interactions of various derivatives of 17β-estradiol (E₂) with human estrogen receptor (ER) α and β. First, we determined the suitability of the molecular docking method to correctly predict the binding modes and interactions of two representative agonists (E₂ and diethylstilbesterol) in the ligand binding domain (LBD) of human ERα. We showed that the docked structures of E₂ and diethylstilbesterol in the ERα LBD were almost exactly the same as the known crystal structures of ERα in complex with these two estrogens. Using the same docking approach, we then characterized the binding interactions of 27 structurally similar E₂ derivatives with the LBDs of human ERα and ERβ. While the binding modes of these E₂ derivatives are very similar to that of E₂, there are distinct subtle differences, and these small differences contribute importantly to their differential binding affinities for ERs. In the case of A-ring estrogen derivatives, there is a strong inverse relationship between the length of the hydrogen bonds formed with ERs and their binding affinity. We found that a better correlation between the computed binding energy values and the experimentally determined logRBA values could be achieved for various A-ring derivatives by re-adjusting the relative weights of the van der Waals interaction energy and the Coulomb interaction energy in computing the overall binding energy values.

Lead optimization mapper: automating free energy calculations for lead optimization

Alchemical free energy calculations hold increasing promise as an aid to drug discovery efforts. However, applications of these techniques in discovery projects have been relatively few, partly because of the difficulty of planning and setting up calculations. Here, we introduce lead optimization mapper, LOMAP, an automated algorithm to plan efficient relative free energy calculations between potential ligands within a substantial library of perhaps hundreds of compounds. In this approach, ligands are first grouped by structural similarity primarily based on the size of a (loosely defined) maximal common substructure, and then calculations are planned within and between sets of structurally related compounds. An emphasis is placed on ensuring that relative free energies can be obtained between any pair of compounds without combining the results of too many different relative free energy calculations (to avoid accumulation of error) and by providing some redundancy to allow for the possibility of error and consistency checking and provide some insight into when results can be expected to be unreliable. The algorithm is discussed in detail and a Python implementation, based on both Schrödinger's and OpenEye's APIs, has been made available freely under the BSD license.

Designing a ligand for pharmaceutical purposes

BACKGROUND

Drug approval applications to the FDA have shown a remarkably small increment compared with what was expected. In the last few years several efforts have been made to improve the results of rational drug design approaches and in particular to predict inhibitor-target structure and to evaluate the free energy of binding. Virtual database screening, combined with other computational methods, is one of the most promising methods to overcome this key issue.

OBJECTIVE

It is possible to understand how computational medicinal chemistry is changing, improving from its errors and moving towards becoming a more important tool for drug development.

METHODS

Some of the most recent modeling techniques have been presented and in particular the benefits of combining these techniques are highlighted.

RESULTS/CONCLUSION

At present computational chemists can understand the peculiar problems associated with the study of biological systems and on this basis they can choose the right collection of complementary in silico approaches to solve the medicinal chemistry problem in a better manner.

Perspective: Alchemical free energy calculations for drug discovery

Computational techniques see widespread use in pharmaceutical drug discovery, but typically prove unreliable in predicting trends in protein-ligand binding. Alchemical free energy calculations seek to change that by providing rigorous binding free energies from molecular simulations. Given adequate sampling and an accurate enough force field, these techniques yield accurate free energy estimates. Recent innovations in alchemical techniques have sparked a resurgence of interest in these calculations. Still, many obstacles stand in the way of their routine application in a drug discovery context, including the one we focus on here, sampling. Sampling of binding modes poses a particular challenge as binding modes are often separated by large energy barriers, leading to slow transitions. Binding modes are difficult to predict, and in some cases multiple binding modes may contribute to binding. In view of these hurdles, we present a framework for dealing carefully with uncertainty in binding mode or conformation in the context of free energy calculations. With careful sampling, free energy techniques show considerable promise for aiding drug discovery.

Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking

A key metric to assess molecular docking remains ligand enrichment against challenging decoys. Whereas the directory of useful decoys (DUD) has been widely used, clear areas for optimization have emerged. Here we describe an improved benchmarking set that includes more diverse targets such as GPCRs and ion channels, totaling 102 proteins with 22886 clustered ligands drawn from ChEMBL, each with 50 property-matched decoys drawn from ZINC. To ensure chemotype diversity, we cluster each target’s ligands by their Bemis–Murcko atomic frameworks. We add net charge to the matched physicochemical properties and include only the most dissimilar decoys, by topology, from the ligands. An online automated tool (http://decoys.docking.org) generates these improved matched decoys for user-supplied ligands. We test this data set by docking all 102 targets, using the results to improve the balance between ligand desolvation and electrostatics in DOCK 3.6. The complete DUD-E benchmarking set is freely available at http://dude.docking.org.

Essential considerations for using protein-ligand structures in drug discovery

Protein-ligand structures are the core data required for structure-based drug design (SBDD). Understanding the error present in this data is essential to the successful development of SBDD tools. Methods for assessing protein-ligand structure quality and a new set of identification criteria are presented here. When these criteria were applied to a set of 728 structures previously used to validate molecular docking software, only 17% were found to be acceptable. Structures were re-refined to maintain internal consistency in the comparison and assessment of the quality criteria. This process resulted in Iridium, a highly trustworthy protein-ligand structure database to be used for development and validation of structure-based design tools for drug discovery.