Docking performance of fragments and druglike compounds

Accurate prediction of dynamic protein-ligand binding using P-score ranking

Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.

Assessment of fragment docking and scoring with the endothiapepsin model system

Fragment-based screening has become indispensable in drug discovery. Yet, the weak binding affinities of these small molecules still represent a challenge for the reliable detection of fragment hits. The extent of this issue was illustrated in the literature for the aspartic protease endothiapepsin: When seven biochemical and biophysical in vitro screening methods were applied to screen a library of 361 fragments, very poor overlap was observed between the hit fragments identified by the individual approaches, resulting in high levels of false positive and/or false negative results depending on the mutually compared methods. Here, the reported in vitro findings are juxtaposed with the results from in silico docking and scoring approaches. The docking programs GOLD and Glide were considered with the scoring functions ASP, ChemScore, ChemPLP, GoldScore, DSXCSD, and GlideScore. First, the ranking power and scoring power were assessed for the named scoring functions. Second, the capability of reproducing the crystallized fragment binding modes was tested in a structure-based redocking approach. The redocking success notably depended on the ligand efficiency of the considered fragments. Third, a blinded virtual screening approach was employed to evaluate whether in silico screening can compete with in vitro methods in the enrichment of fragment databases.

Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin

Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.

Protein Structure-Based Organic Chemistry-Driven Ligand Design from Ultralarge Chemical Spaces

Ultralarge chemical spaces describing several billion compounds are revolutionizing hit identification in early drug discovery. Because of their size, such chemical spaces cannot be fully enumerated and require ad-hoc computational tools to navigate them and pick potentially interesting hits. We here propose a structure-based approach to ultralarge chemical space screening in which commercial chemical reagents are first docked to the target of interest and then directly connected according to organic chemistry and topological rules, to enumerate drug-like compounds under three-dimensional constraints of the target. When applied to bespoke chemical spaces of different sizes and chemical complexity targeting two receptors of pharmaceutical interest (estrogen β receptor, dopamine D3 receptor), the computational method was able to quickly enumerate hits that were either known ligands (or very close analogs) of targeted receptors as well as chemically novel candidates that could be experimentally confirmed by in vitro binding assays. The proposed approach is generic, can be applied to any docking algorithm, and requires few computational resources to prioritize easily synthesizable hits from billion-sized chemical spaces.

Redocking the PDB

Molecular docking is a standard technique in structure-based drug design (SBDD). It aims to predict the 3D structure of a small molecule in the binding site of a receptor (often a protein). Despite being a common technique, it often necessitates multiple tools and involves manual steps. Here, we present the JAMDA preprocessing and docking workflow that is easy to use and allows fully automated docking. We evaluate the JAMDA docking workflow on binding sites extracted from the complete PDB and derive key factors determining JAMDA's docking performance. With that, we try to remove most of the bias due to manual intervention and provide a realistic estimate of the redocking performance of our JAMDA preprocessing and docking workflow for any PDB structure. On this large PDBScan22 data set, our JAMDA workflow finds a pose with an RMSD of at most 2 Å to the crystal ligand on the top rank for 30.1% of the structures. When applying objective structure quality filters to the PDBScan22 data set, the success rate increases to 61.8%. Given the prepared structures from the JAMDA preprocessing pipeline, both JAMDA and the widely used AutoDock Vina perform comparably on this filtered data set (the PDBScan22-HQ data set).

Chemical Space Virtual Screening against Hard-to-Drug RNA Methyltransferases DNMT2 and NSUN6

Targeting RNA methyltransferases with small molecules as inhibitors or tool compounds is an emerging field of interest in epitranscriptomics and medicinal chemistry. For two challenging RNA methyltransferases that introduce the 5-methylcytosine (m⁵C) modification in different tRNAs, namely DNMT2 and NSUN6, an ultra-large commercially available chemical space was virtually screened by physicochemical property filtering, molecular docking, and clustering to identify new ligands for those enzymes. Novel chemotypes binding to DNMT2 and NSUN6 with affinities down to K_D,app = 37 µM and K_D,app = 12 µM, respectively, were identified using a microscale thermophoresis (MST) binding assay. These compounds represent the first molecules with a distinct structure from the cofactor SAM and have the potential to be developed into activity-based probes for these enzymes. Additionally, the challenges and strategies of chemical space docking screens with special emphasis on library focusing and diversification are discussed.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking

We here describe a computational approach (POEM: Pocket Oriented Elaboration of Molecules) to drive the generation of target-focused libraries while taking advantage of all publicly available structural information on protein-ligand complexes. A collection of 31 384 PDB-derived images with key shapes and pharmacophoric properties, describing fragment-bound microenvironments, is first aligned to the query target cavity by a computer vision method. The fragments of the most similar PDB subpockets are then directly positioned in the query cavity using the corresponding image transformation matrices. Lastly, suitable connectable atoms of oriented fragment pairs are linked by a deep generative model to yield fully connected molecules. POEM was applied to generate a library of 1.5 million potential cyclin-dependent kinase 8 inhibitors. By synthesizing and testing as few as 43 compounds, a few nanomolar inhibitors were quickly obtained with limited resources in just two iterative cycles.

Evaluating the use of absolute binding free energy in the fragment optimisation process

Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman's r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely N_wat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.

Implementing a Scoring Function Based on Interaction Fingerprint for Autogrow4: Protein Kinase CK1δ as a Case Study

In the last 20 years, fragment-based drug discovery (FBDD) has become a popular and consolidated approach within the drug discovery pipeline, due to its ability to bring several drug candidates to clinical trials, some of them even being approved and introduced to the market. A class of targets that have proven to be particularly suitable for this method is represented by kinases, as demonstrated by the approval of BRAF inhibitor vemurafenib. Within this wide and diverse set of proteins, protein kinase CK1δ is a particularly interesting target for the treatment of several widespread neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Computational methodologies, such as molecular docking, are already routinely and successfully applied in FBDD campaigns alongside experimental techniques, both in the hit-discovery and in the hit-optimization stage. Concerning this, the open-source software Autogrow, developed by the Durrant lab, is a semi-automated computational protocol that exploits a combination between a genetic algorithm and a molecular docking software for de novo drug design and lead optimization. In the current work, we present and discuss a modified version of the Autogrow code that implements a custom scoring function based on the similarity between the interaction fingerprint of investigated compounds and a crystal reference. To validate its performance, we performed both a de novo and a lead-optimization run (as described in the original publication), evaluating the ability of our fingerprint-based protocol to generate compounds similar to known CK1δ inhibitors based on both the predicted binding mode and the electrostatic and shape similarity in comparison with the standard Autogrow protocol.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

Fragment hopping protocol for the design of small-molecule protein-protein interaction inhibitors

Fragment-based ligand discovery (FBLD) is one of the most successful approaches to designing small-molecule protein-protein interaction (PPI) inhibitors. The incorporation of computational tools to FBLD allows the exploration of chemical space in a time- and cost-efficient manner. Herein, a computational protocol for the development of small-molecule PPI inhibitors using fragment hopping, a fragment-based de novo design approach, is described and a case study is presented to illustrate the efficiency of this protocol. Fragment hopping facilitates the design of PPI inhibitors from scratch solely based on key binding features in the PPI complex structure. This approach is an open system that enables the inclusion of different state-of-the-art programs and softwares to improve its performances.

Validation of an allosteric binding site of Src kinase identified by unbiased ligand binding simulations

Allostery plays a primary role in regulating protein activity, making it an important mechanism in human disease and drug discovery. Identifying allosteric regulatory sites to explore their biological significance and therapeutic potential is invaluable to drug discovery; however, identification remains a challenge. Allosteric sites are often "cryptic" without clear geometric or chemical features. Since allosteric regulatory sites are often less conserved in protein kinases than the orthosteric ATP binding site, allosteric ligands are commonly more specific than ATP competitive inhibitors. We present a generalizable computational protocol to predict allosteric ligand binding sites based on unbiased ligand binding simulation trajectories. We demonstrate the feasibility of this protocol by revisiting our previously published ligand binding simulations using the first identified viral proto-oncogene, Src kinase, as a model system. The binding paths for kinase inhibitor PP1 uncovered three metastable intermediate states before binding the high-affinity ATP-binding pocket, revealing two previously known allosteric sites and one novel site. Herein, we validate the novel site using a combination of virtual screening and experimental assays to identify a v-type allosteric small-molecule inhibitor that targets this novel site with specificity for Src over closely related kinases. This study provides a proof-of-concept for employing unbiased ligand binding simulations to identify cryptic allosteric binding sites and is widely applicable to other protein-ligand systems.

Drug Discovery of Plausible Lead Natural Compounds That Target the Insulin Signaling Pathway: Bioinformatics Approaches

The growing smooth talk in the field of natural compounds is due to the ancient and current interest in herbal medicine and their potentially positive effects on health. Dozens of antidiabetic natural compounds were reported and tested in vivo, in silico, and in vitro. The role of these natural compounds, their actions on the insulin signaling pathway, and the stimulation of the glucose transporter-4 (GLUT4) insulin-responsive translocation to the plasma membrane (PM) are all crucial in the treatment of diabetes and insulin resistance. In this review, we collected and summarized a group of available in vivo and in vitro studies which targeted isolated phytochemicals with possible antidiabetic activity. Moreover, the in silico docking of natural compounds with some of the insulin signaling cascade key proteins is also summarized based on the current literature. In this review, hundreds of recent studies on pure natural compounds that alleviate type II diabetes mellitus (type II DM) were revised. We focused on natural compounds that could potentially regulate blood glucose and stimulate GLUT4 translocation through the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) pathway. On attempt to point out potential new natural antidiabetic compounds, this review also focuses on natural ingredients that were shown to interact with proteins in the insulin signaling pathway in silico, regardless of their in vitro/in vivo antidiabetic activity. We invite interested researchers to test these compounds as potential novel type II DM drugs and explore their therapeutic mechanisms.

Re-Exploring the Ability of Common Docking Programs to Correctly Reproduce the Binding Modes of Non-Covalent Inhibitors of SARS-CoV-2 Protease Mpro

In the latest few decades, molecular docking has imposed itself as one of the most used approaches for computational drug discovery. Several docking benchmarks have been published, comparing the performance of different algorithms in respect to a molecular target of interest, usually evaluating their ability in reproducing the experimental data, which, in most cases, comes from X-ray structures. In this study, we elucidated the variation of the performance of three docking algorithms, namely GOLD, Glide, and PLANTS, in replicating the coordinates of the crystallographic ligands of SARS-CoV-2 main protease (Mpro). Through the comparison of the data coming from docking experiments and the values derived from the calculation of the solvent exposure of the crystallographic ligands, we highlighted the importance of this last variable for docking performance. Indeed, we underlined how an increase in the percentage of the ligand surface exposed to the solvent in a crystallographic complex makes it harder for the docking algorithms to reproduce its conformation. We further validated our hypothesis through molecular dynamics simulations, showing that the less stable protein–ligand complexes (in terms of root-mean-square deviation and root-mean-square fluctuation) tend to be derived from the cases in which the solvent exposure of the ligand in the starting system is higher.

HT-SuMD: making molecular dynamics simulations suitable for fragment-based screening. A comparative study with NMR

Fragment-based lead discovery (FBLD) is one of the most efficient methods to develop new drugs. We present here a new computational protocol called High-Throughput Supervised Molecular Dynamics (HT-SuMD), which makes it possible to automatically screen up to thousands of fragments, representing therefore a new valuable resource to prioritise fragments in FBLD campaigns. The protocol was applied to Bcl-XL, an oncological protein target involved in the regulation of apoptosis through protein-protein interactions. Initially, HT-SuMD performances were validated against a robust NMR-based screening, using the same set of 100 fragments. These independent results showed a remarkable agreement between the two methods. Then, a virtual screening on a larger library of additional 300 fragments was carried out and the best hits were validated by NMR. Remarkably, all the in silico selected fragments were confirmed as Bcl-XL binders. This represents, to date, the largest computational fragments screening entirely based on MD.

Use of molecular docking computational tools in drug discovery

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

Inspecting the Mechanism of Fragment Hits Binding on SARS-CoV-2 Mpro by Using Supervised Molecular Dynamics (SuMD) Simulations

Computational approaches supporting the early characterization of fragment molecular recognition mechanism represent a valuable complement to more expansive and low‐throughput experimental techniques. In this retrospective study, we have investigated the geometric accuracy with which high‐throughput supervised molecular dynamics simulations (HT‐SuMD) can anticipate the experimental bound state for a set of 23 fragments targeting the SARS‐CoV‐2 main protease. Despite the encouraging results herein reported, in line with those previously described for other MD‐based posing approaches, a high number of incorrect binding modes still complicate HT‐SuMD routine application. To overcome this limitation, fragment pose stability has been investigated and integrated as part of our in‐silico pipeline, allowing us to prioritize only the more reliable predictions.

The rise of molecular simulations in fragment-based drug design (FBDD): an overview

Fragment-based drug discovery (FBDD) is an innovative approach, progressively more applied in the academic and industrial context, to enhance hit identification for previously considered undruggable biological targets. In particular, FBDD discovers low-molecular-weight (LMW) ligands (<300Da) able to bind to therapeutically relevant macromolecules in an affinity range from the micromolar (μM) to millimolar (mM). X-ray crystallography (XRC) and nuclear magnetic resonance (NMR) spectroscopy are commonly the methods of choice to obtain 3D information about the bound ligand-protein complex, but this can occasionally be problematic, mainly for early, low-affinity fragments. The recent development of computational fragment-based approaches provides a further strategy for improving the identification of fragment hits. In this review, we summarize the state of the art of molecular dynamics simulations approaches used in FBDD, and discuss limitations and future perspectives for these approaches.

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.

The fine art of preparing membrane transport proteins for biomolecular simulations: Concepts and practical considerations

Molecular dynamics (MD) simulations have developed into an invaluable tool in bimolecular research, due to the capability of the method in capturing molecular events and structural transitions that describe the function as well as the physiochemical properties of biomolecular systems. Due to the progressive development of more efficient algorithms, expansion of the available computational resources, as well as the emergence of more advanced methodologies, the scope of computational studies has increased vastly over time. We now have access to a multitude of online databases, software packages, larger molecular systems and novel ligands due to the phenomenon of emerging novel psychoactive substances (NPS). With so many advances in the field, it is understandable that novices will no doubt find it challenging setting up a protein-ligand system even before they run their first MD simulation. These initial steps, such as homology modelling, ligand docking, parameterization, protein preparation and membrane setup have become a fundamental part of the drug discovery pipeline, and many areas of biomolecular sciences benefit from the applications provided by these technologies. However, there still remains no standard on their usage. Therefore, our aim within this review is to provide a clear overview of a variety of concepts and methodologies to consider, providing a workflow for a case study of a membrane transport protein, the full-length human dopamine transporter (hDAT) in complex with different stimulants, where MD simulations have recently been applied successfully.

Fragment- and negative image-based screening of phosphodiesterase 10A inhibitors

A novel virtual screening methodology called fragment- and negative image-based (F-NiB) screening is introduced and tested experimentally using phosphodiesterase 10A (PDE10A) as a case study. Potent PDE10A-specific small-molecule inhibitors are actively sought after for their antipsychotic and neuroprotective effects. The F-NiB combines features from both fragment-based drug discovery and negative image-based (NIB) screening methodologies to facilitate rational drug discovery. The selected structural parts of protein-bound ligand(s) are seamlessly combined with the negative image of the target's ligand-binding cavity. This cavity- and fragment-based hybrid model, namely its shape and electrostatics, is used directly in the rigid docking of ab initio generated ligand 3D conformers. In total, 14 compounds were acquired using the F-NiB methodology, 3D quantitative structure-activity relationship modeling, and pharmacophore modeling. Three of the small molecules inhibited PDE10A at ~27 to ~67 μM range in a radiometric assay. In a larger context, the study shows that the F-NiB provides a flexible way to incorporate small-molecule fragments into the drug discovery.

Fragment Hits: What do They Look Like and How do They Bind?

A “fragment hit”, a molecule of low molecular weight that has been validated to bind to a target protein, can be an effective chemical starting point for a drug discovery project. Our ability to find and progress fragment hits could potentially be improved by enhancing our understanding of their binding properties, which to date has largely been based on tacit knowledge and reports from individual projects. In the work reported here, we systematically analyzed the molecular and binding properties of fragment hits using 489 published protein–fragment complexes. We identified a number of notable features that these hits tend to have in common, including preferences in buried surface area upon binding, hydrogen bonding and other directional interactions with the protein targets, structural topology, functional-group occurrence, and degree of carbon saturation. In the future, taking account of these preferences in designing and selecting fragments to screen against protein targets may increase the chances of success in fragment screening campaigns.

Drug–DNA interaction, a joint DFT-D3/MD study on safranal as an anticancer and DNA nanostructure model

In this research, using a combination of quantum mechanics and molecular dynamic (MD) simulations, the interaction of safranal (2,6,6-trimethylcyclohexa-1,3-dien-1-carboxaldehyde) as an anti-cancer drug and Dickerson B-DNA was studied. MD simulations were executed for 35 ns in water. Binding energy analysis in three definite parts of the B-DNA and comparison between different contributions of the binding energy shows that the van der Waals energy part of the interaction is impressive among the standard molecular mechanic energy terms. On the basis of Gibbs energies, it is confirmed that the most important interactions in the safranal complex are related to the A–T and C–G rich regions, which is in agreement with the experimental data. Quantum theory of atoms in molecules and natural bond orbital analyses were applied. A diminution in the electronic chemical potential of the safranal–DNA complex in comparison with the isolated DNA, 0.026 and 0.022 au for the S1 region and 0.012 and 0.017 au for the S2 region, was obtained in the gas phase and water, respectively, which increases the complex stability. An enhancement in the electrophilicity character, during the complexation process, shows the electron charge flux between the safranal and DNA, especially in water. The strengths of the CH⋯O bonds at the center of safranal–DNA interaction were also evaluated. A mean value of 0.06 au for the electron density of the bond critical point of the H⋯O in the complex confirms the H-bond formation during the complexation.

In silico identification and experimental validation of hits active against KPC-2 β-lactamase

Bacterial resistance has become a worldwide concern, particularly after the emergence of resistant strains overproducing carbapenemases. Among these, the KPC-2 carbapenemase represents a significant clinical challenge, being characterized by a broad substrate spectrum that includes aminothiazoleoxime and cephalosporins such as cefotaxime. Moreover, strains harboring KPC-type β-lactamases are often reported as resistant to available β-lactamase inhibitors (clavulanic acid, tazobactam and sulbactam). Therefore, the identification of novel non β-lactam KPC-2 inhibitors is strongly necessary to maintain treatment options. This study explored novel, non-covalent inhibitors active against KPC-2, as putative hit candidates. We performed a structure-based in silico screening of commercially available compounds for non-β-lactam KPC-2 inhibitors. Thirty-two commercially available high-scoring, fragment-like hits were selected for in vitro validation and their activity and mechanism of action vs the target was experimentally evaluated using recombinant KPC-2. N-(3-(1H-tetrazol-5-yl)phenyl)-3-fluorobenzamide (11a), in light of its ligand efficiency (LE = 0.28 kcal/mol/non-hydrogen atom) and chemistry, was selected as hit to be directed to chemical optimization to improve potency vs the enzyme and explore structural requirement for inhibition in KPC-2 binding site. Further, the compounds were evaluated against clinical strains overexpressing KPC-2 and the most promising compound reduced the MIC of the β-lactam antibiotic meropenem by four-fold.

High-throughput screening campaigns against a PI3Kα isoform bearing the H1047R mutation identified potential inhibitors with novel scaffolds

The phosphatidylinositol 3-kinase (PI3K) pathway is involved in many cellular functions including cell growth, metabolism, and transformation. Hyperactivation of this pathway contributes to tumorigenesis, therefore, PI3K is a major target for anticancer drug discovery. Since the PI3Kα isoform is implicated mostly in cancer, we conducted a high-throughput screening (HTS) campaign using a 3-step PI3K homogenous time-resolved fluorescence assay against this isoform bearing the H1047R mutation. A total of 288,000 synthetic and natural product-derived compounds were screened and of which, we identified 124 initial hits that were further selected by considering the predicted binding mode, relationship to known pan-assay interference compounds and previous descriptions as a lipid kinase inhibitor. A total of 24 compounds were then tested for concentration-dependent responses. These hit compounds provide novel scaffolds that can potentially be optimized to create novel PI3K inhibitors.

Fragment-Based Drug Discovery: Advancing Fragments in the Absence of Crystal Structures

Fragment-based drug discovery typically requires an interplay between screening methods, structural methods, and medicinal chemistry. X-ray crystallography is generally the method of choice to obtain three-dimensional structures of the bound ligand/protein complex, but this can sometimes be difficult, particularly for early, low-affinity fragment hits. In this Perspective, we discuss strategies to advance and evolve fragments in the absence of crystal structures of protein-fragment complexes, although the structure of the unliganded protein may be available. The strategies can involve other structural techniques, such as NMR spectroscopy, molecular modeling, or a variety of chemical approaches. Often, these strategies are aimed at guiding evolution of initial fragment hits to a stage where crystal structures can be obtained for further structure-based optimization.

In silico fragment-based drug design with SEED

We report on two fragment-based drug design protocols, SEED2XR and ALTA, which start by high-throughput docking. SEED2XR is a two-stage protocol for fragment-based drug design. The first stage is in silico and consists of the automatic docking of 10³-10⁴ fragments using SEED, which requires about 1 s per fragment. SEED is a docking software developed specifically for fragment docking and binding energy evaluation by a force field with implicit solvent. In the second stage of SEED2XR, the 10-10² fragments with the most favorable predicted binding energies are validated by protein X-ray crystallography. The recent applications of SEED2XR to bromodomains demonstrate that the whole SEED2XR protocol can be carried out in about a week of working time, with hit rates ranging from 10% to 40%. Information on fragment-target interactions generated by the SEED2XR protocol or directly from SEED docking has been used for the discovery of hundreds of hits. ALTA is a computational protocol for screening which identifies candidate ligands that preserve the interactions between the optimal SEED fragments and the protein target. Medicinal chemistry optimization of ligands predicted by ALTA has resulted in pre-clinical candidates for protein kinases and bromodomains. The high-throughput, very low cost, sustainability, and high hit rate of the SEED-based protocols, unreachable by purely experimental techniques, make them perfectly suitable for both academic and industrial drug discovery research.

NMR-Fragment Based Virtual Screening: A Brief Overview

Fragment-based drug discovery (FBDD) using NMR has become a central approach over the last twenty years for development of small molecule inhibitors against biological macromolecules, to control a variety of cellular processes. Yet, several considerations should be taken into account for obtaining a therapeutically relevant agent. In this review, we aim to list the considerations that make NMR fragment screening a successful process for yielding potent inhibitors. Factors that may govern the competence of NMR in fragment based drug discovery are discussed, as well as later steps that involve optimization of hits obtained by NMR-FBDD.

Protein structure-based drug design: from docking to molecular dynamics

Recent years have witnessed rapid developments of computer-aided drug design methods, which have reached accuracy that allows their routine practical applications in drug discovery campaigns. Protein structure-based methods are useful for the prediction of binding modes of small molecules and their relative affinity. The high-throughput docking of up to 10⁶ small molecules followed by scoring based on implicit-solvent force field can robustly identify micromolar binders using a rigid protein target. Molecular dynamics with explicit solvent is a low-throughput technique for the characterization of flexible binding sites and accurate evaluation of binding pathways, kinetics, and thermodynamics. In this review we highlight recent advancements in applications of ligand docking tools and molecular dynamics simulations to ligand identification and optimization.

Novel Benzylidene Thiazolidinedione Derivatives as Partial PPARγ Agonists and their Antidiabetic Effects on Type 2 Diabetes

Peroxisome proliferator-activated receptor γ (PPARγ) has received significant attention as a key regulator of glucose and lipid homeostasis. In this study, we synthesized and tested a library of novel 5-benzylidene-thiazolidin-2,4-dione (BTZD) derivatives bearing a substituent on nitrogen of TZD nucleus (compounds 1a-1k, 2i-10i, 3a, 6a, and 8a-10a). Three compounds (1a, 1i, and 3a) exhibited selectivity towards PPARγ and were found to be weak to moderate partial agonists. Surface Plasmon Resonance (SPR) results demonstrated binding affinity of 1a, 1i and 3a towards PPARγ. Furthermore, docking experiments revealed that BTZDs interact with PPARγ through a distinct binding mode, forming primarily hydrophobic contacts with the ligand-binding pocket (LBD) without direct H-bonding interactions to key residues in H12 that are characteristic of full agonists. In addition, 1a, 1i and 3a significantly improved hyperglycemia and hyperlipidaemia in streptozotocin-nicotinamide (STZ-NA)-induced diabetic rats at a dose of 36 mg/kg/day administered orally for 15 days. Histopathological investigations revealed that microscopic architecture of pancreatic and hepatic cells improved in BTZDs-treated diabetic rats. These findings suggested that 1a, 1i and 3a are very promising pharmacological agents by selectively targeting PPARγ for further development in the clinical treatment of type 2 diabetes mellitus.

Fragment-Based Discovery and Optimization of Enzyme Inhibitors by Docking of Commercial Chemical Space

Fragment-based lead discovery has emerged as a leading drug development strategy for novel therapeutic targets. Although fragment-based drug discovery benefits immensely from access to atomic-resolution information, structure-based virtual screening has rarely been used to drive fragment discovery and optimization. Here, molecular docking of 0.3 million fragments to a crystal structure of cancer target MTH1 was performed. Twenty-two predicted fragment ligands, for which analogs could be acquired commercially, were experimentally evaluated. Five fragments inhibited MTH1 with IC₅₀ values ranging from 6 to 79 μM. Structure-based optimization guided by predicted binding modes and analogs from commercial chemical libraries yielded nanomolar inhibitors. Subsequently solved crystal structures confirmed binding modes predicted by docking for three scaffolds. Structure-guided exploration of commercial chemical space using molecular docking gives access to fragment libraries that are several orders of magnitude larger than those screened experimentally and can enable efficient optimization of hits to potent leads.

Fragment optimization for GPCRs by molecular dynamics free energy calculations: Probing druggable subpockets of the A 2A adenosine receptor binding site

Fragment-based lead discovery is becoming an increasingly popular strategy for drug discovery. Fragment screening identifies weakly binding compounds that require optimization to become high-affinity leads. As design of leads from fragments is challenging, reliable computational methods to guide optimization would be invaluable. We evaluated using molecular dynamics simulations and the free energy perturbation method (MD/FEP) in fragment optimization for the A_2A adenosine receptor, a pharmaceutically relevant G protein-coupled receptor. Optimization of fragments exploring two binding site subpockets was probed by calculating relative binding affinities for 23 adenine derivatives, resulting in strong agreement with experimental data (R² = 0.78). The predictive power of MD/FEP was significantly better than that of an empirical scoring function. We also demonstrated the potential of the MD/FEP to assess multiple binding modes and to tailor the thermodynamic profile of ligands during optimization. Finally, MD/FEP was applied prospectively to optimize three nonpurine fragments, and predictions for 12 compounds were evaluated experimentally. The direction of the change in binding affinity was correctly predicted in a majority of the cases, and agreement with experiment could be improved with rigorous parameter derivation. The results suggest that MD/FEP will become a powerful tool in structure-driven optimization of fragments to lead candidates.

Molecular Docking, Molecular Dynamics, and Structure-Activity Relationship Explorations of 14-Oxygenated N-Methylmorphinan-6-ones as Potent μ-Opioid Receptor Agonists

Among opioids, morphinans are of major importance as the most effective analgesic drugs acting primarily via μ-opioid receptor (μ-OR) activation. Our long-standing efforts in the field of opioid analgesics from the class of morphinans led to N-methylmorphinan-6-ones differently substituted at positions 5 and 14 as μ-OR agonists inducing potent analgesia and fewer undesirable effects. Herein we present the first thorough molecular modeling study and structure–activity relationship (SAR) explorations aided by docking and molecular dynamics (MD) simulations of 14-oxygenated N-methylmorphinan-6-ones to gain insights into their mode of binding to the μ-OR and interaction mechanisms. The structure of activated μ-OR provides an essential model for how ligand/μ-OR binding is encoded within small chemical differences in otherwise structurally similar morphinans. We reveal important molecular interactions that these μ-agonists share and distinguish them. The molecular docking outcomes indicate the crucial role of the relative orientation of the ligand in the μ-OR binding site, influencing the propensity of critical non-covalent interactions that are required to facilitate ligand/μ-OR interactions and receptor activation. The MD simulations point out minor differences in the tendency to form hydrogen bonds by the 4,5α-epoxy group, along with the tendency to affect the 3–7 lock switch. The emerged SARs reveal the subtle interplay between the substituents at positions 5 and 14 in the morphinan scaffold by enabling the identification of key structural elements that determine the distinct pharmacological profiles. This study provides a significant structural basis for understanding ligand binding and μ-OR activation by the 14-oxygenated N-methylmorphinan-6-ones, which should be useful for guiding drug design.

Exploring the Role of N6-Substituents in Potent Dual Acting 5'-C-Ethyltetrazolyladenosine Derivatives: Synthesis, Binding, Functional Assays, and Antinociceptive Effects in Mice ∇

Structural determinants of affinity of N⁶-substituted-5'-C-(ethyltetrazol-2-yl)adenosine and 2-chloroadenosine derivatives at adenosine receptor (AR) subtypes were studied with binding and molecular modeling. Small N⁶-cycloalkyl and 3-halobenzyl groups furnished potent dual acting A₁AR agonists and A₃AR antagonists. 4 was the most potent dual acting human (h) A₁AR agonist (K_i = 0.45 nM) and A₃AR antagonist (K_i = 0.31 nM) and highly selective versus A_2A; 11 and 26 were most potent at both h and rat (r) A₃AR. All N⁶-substituted-5'-C-(ethyltetrazol-2-yl)adenosine derivatives proved to be antagonists at hA₃AR but agonists at the rA₃AR. Analgesia of 11, 22, and 26 was evaluated in the mouse formalin test (A₃AR antagonist blocked and A₃AR agonist strongly potentiated). N⁶-Methyl-5'-C-(ethyltetrazol-2-yl)adenosine (22) was most potent, inhibiting both phases, as observed combining A₁AR and A₃AR agonists. This study demonstrated for the first time the advantages of a single molecule activating two AR pathways both leading to benefit in this acute pain model.

Molecular dynamic simulation and DFT study on the Drug-DNA interaction; Crocetin as an anti-cancer and DNA nanostructure model

In this research, the interaction of Crocetin as an anti-cancer drug and a Dickerson DNA has been investigated. 25 ns molecular dynamic simulations of Crocetin and DNA composed of 12 base pairs and a sequence of d(CGCGAATTCGCG)₂ were done in water. Three definite parts of the B-DNA were considered in analyzing the best interactive site from the thermodynamic point of view. Binding energy analysis showed that van der Waals interaction is the most important part related to the reciprocal O and H atoms of the Crocetin and DNA. Stabilizing interactions, obtained by ΔG calculations, showed that maximum and minimum interactions are related to the S1 and S3 regions, respectively. This means that the most probable van der Waals interaction site of the Dickerson B-DNA and Crocetin is located in the minor groove of DNA. Two sharp peaks at 2.55 and 1.75 Å in radial distribution functions of the PO⋯HO and NH⋯OC parts are related to new hydrogen bonds between the Crocetin and DNA in the complex which can be considered as the driving force of the anti-cancer mechanism of the Crocetin. Average values of 0.3 au and zero for the electron densities of the H⋯O bonds for DNA and complex, obtained by Quantum theory of atoms in molecules (QTAIM), means that the origin of DNA instability after complexation may be related to the H-bond denaturation by Crocetin. Finally, the evaluation of the dispersion interactions using the dispersion functional, -148.76 kcal.mol^-1, confirmed the importance of the dispersion interaction in drug-DNA complex.

Computational Multitarget Drug Design

Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.

Function-specific virtual screening for GPCR ligands using a combined scoring method

The ability of scoring functions to correctly select and rank docking poses of small molecules in protein binding sites is highly target dependent, which presents a challenge for structure-based drug discovery. Here we describe a virtual screening method that combines an energy-based docking scoring function with a molecular interaction fingerprint (IFP) to identify new ligands based on G protein-coupled receptor (GPCR) crystal structures. The consensus scoring method is prospectively evaluated by: 1) the discovery of chemically novel, fragment-like, high affinity histamine H₁ receptor (H₁R) antagonists/inverse agonists, 2) the selective structure-based identification of ß₂-adrenoceptor (ß₂R) agonists, and 3) the experimental validation and comparison of the combined and individual scoring approaches. Systematic retrospective virtual screening simulations allowed the definition of scoring cut-offs for the identification of H₁R and ß₂R ligands and the selection of an optimal ß-adrenoceptor crystal structure for the discrimination between ß₂R agonists and antagonists. The consensus approach resulted in the experimental validation of 53% of the ß₂R and 73% of the H₁R virtual screening hits with up to nanomolar affinities and potencies. The selective identification of ß₂R agonists shows the possibilities of structure-based prediction of GPCR ligand function by integrating protein-ligand binding mode information.