Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators.

Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target's binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low µM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.

Ligand-Enhanced Negative Images Optimized for Docking Rescoring.

Despite the pivotal role of molecular docking in modern drug discovery, the default docking scoring functions often fail to recognize active ligands in virtual screening campaigns. Negative image-based rescoring improves docking enrichment by comparing the shape/electrostatic potential (ESP) of the flexible docking poses against the target protein's inverted cavity volume. By optimizing these negative image-based (NIB) models using a greedy search, the docking rescoring yield can be improved massively and consistently. Here, a fundamental modification is implemented to this shape-focused pharmacophore modelling approach-actual ligand 3D coordinates are incorporated into the NIB models for the optimization. This hybrid approach, labelled as ligand-enhanced brute-force negative image-based optimization (LBR-NiB), takes the best from both worlds, i.e., the all-roundedness of the NIB models and the difficult to emulate atomic arrangements of actual protein-bound small-molecule ligands. Thorough benchmarking, focused on proinflammatory targets, shows that the LBR-NiB routinely improves the docking enrichment over prior iterations of the R-NiB methodology. This boost can be massive, if the added ligand information provides truly essential binding information that was lacking or completely missing from the cavity-based NIB model. On a practical level, the results indicate that the LBR-NiB typically works well when the added ligand 3D data originates from a high-quality source, such as X-ray crystallography, and, yet, the NIB model compositions can also sometimes be improved by fusing into them, for example, with flexibly docked solvent molecules. In short, the study demonstrates that the protein-bound ligands can be used to improve the shape/ESP features of the negative images for effective docking rescoring use in virtual screening.

Rational design, optimization, and biological evaluation of novel α-Phosphonopropionic acids as covalent inhibitors of Rab geranylgeranyl transferase.

Rab geranylgeranyltransferase (GGTase-II, RGGT) catalyses the post-translational modification of eukaryotic Rab GTPases, proteins implicated in several pathologies, including cancer, diabetes, neurodegenerative, and infectious diseases. Thus, RGGT inhibitors are believed to be a potential platform for the development of drugs and tools for studying processes related to the abnormal activity of Rab GTPases. Here, a series of new α-phosphonocarboxylates have been prepared in the first attempt of rational design of covalent inhibitors of RGGT derived from non-covalent inhibitors. These compounds were equipped with electrophilic groups capable of binding cysteines, which are present in the catalytic cavity of RGGT. A few of these analogues have shown micromolar activity against RGGT, which correlated with their ability to inhibit the proliferation of the HeLa cancer cell line. The proposed mechanism of this inhibitory activity was rationalised by molecular docking and mass spectrometric measurements, supported by stability and reactivity studies.

Computational Study on the Inhibitory Effect of Natural Compounds against the SARS-CoV-2 Proteins.

COVID-19 is more virulent and challenging to human life. In India, the Ministry of AYUSH recommended some strategies through Siddha, homeopathy, and other methods to effectively manage COVID-19 (Guidelines for AYUSH Clinical Studies in COVID-19, 2020). Kabasura Kudineer and homeopathy medicines are in use for the prevention and treatment of COVID-19 infection; however, the mechanism of action is less explored. This study aims to understand the antagonist activity of natural compounds found in Kabasura Kudineer and homeopathy medicines against the SARS-CoV-2 using computational methods. Potential compounds were screened against NSP-12, NSP-13, NSP-14, NSP-15, main protease, and spike proteins. Structure-based virtual screening results shows that, out of 14,682 Kabasura Kudineer compounds, the 250395, 129677029, 44259583, 44259584, and 88583189 compounds and, out of 3,112 homeopathy compounds, the 3802778, 320361, 5315832, 14590080, and 74029795 compounds have good scoring function against the SARS-CoV-2 structural and nonstructural proteins. As a result of docking, homeopathy compounds have a docking score ranging from -5.636 to 13.631 kcal/mol, while Kabasura Kudineer compounds have a docking score varying from -8.290 to -13.759 kcal/mol. It has been found that the selected compounds bind well to the active site of SARS-CoV-2 proteins and form hydrogen bonds. The molecular dynamics simulation study shows that the selected compounds have maintained stable conformation in the simulation period and interact with the target. This study supports the antagonist activity of natural compounds from Kabasura Kudineer and homeopathy against SARS-CoV-2's structural and nonstructural proteins.

Optimization of Cavity-Based Negative Images to Boost Docking Enrichment in Virtual Screening.

Molecular docking is a key in silico method used routinely in modern drug discovery projects. Although docking provides high-quality ligand binding predictions, it regularly fails to separate the active compounds from the inactive ones. In negative image-based rescoring (R-NiB), the shape/electrostatic potential (ESP) of docking poses is compared to the negative image of the protein's ligand binding cavity. While R-NiB often improves the docking yield considerably, the cavity-based models do not reach their full potential without expert editing. Accordingly, a greedy search-driven methodology, brute force negative image-based optimization (BR-NiB), is presented for optimizing the models via iterative editing and benchmarking. Thorough and unbiased training, testing and stringent validation with a multitude of drug targets, and alternative docking software show that BR-NiB ensures excellent docking efficacy. BR-NiB can be considered as a new type of shape-focused pharmacophore modeling, where the optimized models contain only the most vital cavity information needed for effectively filtering docked actives from the inactive or decoy compounds. Finally, the BR-NiB code for performing the automated optimization is provided free-of-charge under MIT license via GitHub (https://github.com/jvlehtonen/brutenib) for boosting the success rates of docking-based virtual screening campaigns.

Sdfconf: A Novel, Flexible, and Robust Molecular Data Management Tool.

Projects in chemo- and bioinformatics often consist of scattered data in various types and are difficult to access in a meaningful way for efficient data analysis. Data is usually too diverse to be even manipulated effectively. Sdfconf is data manipulation and analysis software to address this problem in a logical and robust manner. Other software commonly used for such tasks are either not designed with molecular and/or conformational data in mind or provide only a narrow set of tasks to be accomplished. Furthermore, many tools are only available within commercial software packages. Sdfconf is a flexible, robust, and free-of-charge tool for linking data from various sources for meaningful and efficient manipulation and analysis of molecule data sets. Sdfconf packages molecular structures and metadata into a complete ensemble, from which one can access both the whole data set and individual molecules and/or conformations. In this software note, we offer some practical examples of the utilization of sdfconf.

A holistic view on c-Kit in cancer: Structure, signaling, pathophysiology and its inhibitors.

Receptor tyrosine kinases play an important role in many cellular processes, and their dysregulation leads to diseases, most importantly cancer. One such receptor tyrosine kinase is c-Kit, a type-III receptor tyrosine kinase, which is involved in various intracellular signaling pathways. The role of different mutant isoforms of c-Kit has been established in several types of cancers. Accordingly, promising c-Kit inhibition results have been reported for the treatment of different cancers (e.g., gastrointestinal stromal tumors, melanoma, acute myeloid leukemia, and other tumors). Therefore, lots of effort has been put to target c-Kit for the treatment of cancer. Here, we provide a comprehensive compilation to provide an insight into c-Kit inhibitor discovery. This compilation provides key information regarding the structure, signaling pathways related to c-Kit, and, more importantly, pharmacophores, binding modes, and SAR analysis for almost all small-molecule heterocycles reported for their c-Kit inhibitory activity. This work could be used as a guide in understanding the basic requirements for targeting c-Kit, and how the selectivity and efficacy of the molecules have been achieved till today.

Coumarins as Tool Compounds to Aid the Discovery of Selective Function Modulators of Steroid Hormone Binding Proteins.

Steroid hormones play an essential role in a wide variety of actions in the body, such as in metabolism, inflammation, initiating and maintaining sexual differentiation and reproduction, immune functions, and stress response. Androgen, aromatase, and sulfatase pathway enzymes and nuclear receptors are responsible for steroid biosynthesis and sensing steroid hormones. Changes in steroid homeostasis are associated with many endocrine diseases. Thus, the discovery and development of novel drug candidates require a detailed understanding of the small molecule structure-activity relationship with enzymes and receptors participating in steroid hormone synthesis, signaling, and metabolism. Here, we show that simple coumarin derivatives can be employed to build cost-efficiently a set of molecules that derive essential features that enable easy discovery of selective and high-affinity molecules to target proteins. In addition, these compounds are also potent tool molecules to study the metabolism of any small molecule.

Substrate Selectivity of Coumarin Derivatives by Human CYP1 Enzymes: In Vitro Enzyme Kinetics and In Silico Modeling.

Of the three enzymes in the human cytochrome P450 family 1, CYP1A2 is an important enzyme mediating metabolism of xenobiotics including drugs in the liver, while CYP1A1 and CYP1B1 are expressed in extrahepatic tissues. Currently used CYP substrates, such as 7-ethoxycoumarin and 7-ethoxyresorufin, are oxidized by all individual CYP1 forms. The main aim of this study was to find profluorescent coumarin substrates that are more selective for the individual CYP1 forms. Eleven 3-phenylcoumarin derivatives were synthetized, their enzyme kinetic parameters were determined, and their interactions in the active sites of CYP1 enzymes were analyzed by docking and molecular dynamic simulations. All coumarin derivatives and 7-ethoxyresorufin and 7-pentoxyresorufin were oxidized by at least one CYP1 enzyme. 3-(3-Methoxyphenyl)-6-methoxycoumarin (19) was 7-O-demethylated by similar high efficiency [21-30 ML/(min·mol CYP)] by all CYP1 forms and displayed similar binding in the enzyme active sites. 3-(3-Fluoro-4-acetoxyphenyl)coumarin (14) was selectively 7-O-demethylated by CYP1A1, but with low efficiency [0.16 ML/(min mol)]. This was explained by better orientation and stronger H-bond interactions in the active site of CYP1A1 than that of CYP1A2 and CYP1B1. 3-(4-Acetoxyphenyl)-6-chlorocoumarin (20) was 7-O-demethylated most efficiently by CYP1B1 [53 ML/(min·mol CYP)], followed by CYP1A1 [16 ML/(min·mol CYP)] and CYP1A2 [0.6 ML/(min·mol CYP)]. Variations in stabilities of complexes between 20 and the individual CYP enzymes explained these differences. Compounds 14, 19, and 20 are candidates to replace traditional substrates in measuring activity of human CYP1 enzymes.

Detection of Binding Sites on SARS-CoV-2 Spike Protein Receptor-Binding Domain by Molecular Dynamics Simulations in Mixed Solvents.

The novel SARS-CoV-2 uses ACE2 (Angiotensin-Converting Enzyme 2) receptor as an entry point. Insights on S protein receptor-binding domain (RBD) interaction with ACE2 receptor and drug repurposing has accelerated drug discovery for the novel SARS-CoV-2 infection. Finding small molecule binding sites in S protein and ACE2 interface is crucial in search of effective drugs to prevent viral entry. In this study, we employed molecular dynamics simulations in mixed solvents together with virtual screening to identify small molecules that could be potential inhibitors of S protein -ACE2 interaction. Observation of organic probe molecule localization during the simulations revealed multiple sites at the S protein surface related to small molecule, antibody, and ACE2 binding. In addition, a novel conformation of the S protein was discovered that could be stabilized by small molecules to inhibit attachment to ACE2. The most promising binding site on RBD-ACE2 interface was targeted with virtual screening and top-ranked compounds (DB08248, DB02651, DB03714, and DB14826) are suggested for experimental testing. The protocol described here offers an extremely fast method for characterizing key proteins of a novel pathogen and for the identification of compounds that could inhibit or accelerate spreading of the disease.

Molecular docking and oxidation kinetics of 3-phenyl coumarin derivatives by human CYP2A13.

CYP2A13 enzyme is expressed in human extrahepatic tissues, while CYP2A6 is a hepatic enzyme. Reactions catalysed by CYP2A13 activate tobacco-specific nitrosamines and some other toxic xenobiotics in lungs.To compare oxidation characteristics and substrate-enzyme active site interactions in CYP2A13 vs CYP2A6, we evaluated CYP2A13 mediated oxidation characteristics of 23 coumarin derivatives and modelled their interactions at the enzyme active site.CYP2A13 did not oxidise six coumarin derivatives to corresponding fluorescent 7-hydroxycoumarins. The Km-values of the other coumarins varied 0.85-97 µM, Vmax-values of the oxidation reaction varied 0.25-60 min-1, and intrinsic clearance varied 26-6190 kL/min*mol CYP2A13). Km of 6-chloro-3-(3-hydroxyphenyl)-coumarin was 0.85 (0.55-1.15 95% confidence limit) µM and Vmax 0.25 (0.23-0.26) min-1, whereas Km of 6-hydroxy-3-(3-hydroxyphenyl)-coumarin was 10.9 (9.9-11.8) µM and Vmax 60 (58-63) min-1. Docking analyses demonstrated that 6-chloro or 6-methoxy and 3-(3-hydroxyphenyl) or 3-(4-trifluoromethylphenyl) substituents of coumarin increased affinity to CYP2A13, whereas 3-triazole or 3-(3-acetate phenyl) or 3-(4-acetate phenyl) substituents decreased it.The active site of CYP2A13 accepts more diversified types of coumarin substrates than the hepatic CYP2A6 enzyme. New sensitive and convenient profluorescent CYP2A13 substrates were identified, such as 6-chloro-3-(3-hydroxyphenyl)-coumarin having high affinity and 6-hydroxy-3-(3-hydroxyphenyl)-coumarin with high intrinsic clearance.

Negative Image-Based Screening: Rigid Docking Using Cavity Information.

Rational drug discovery relies heavily on molecular docking-based virtual screening, which samples flexibly the ligand binding poses against the target protein's structure. The upside of flexible docking is that the geometries of the generated docking poses are adjusted to match the residue alignment inside the target protein's ligand-binding pocket. The downside is that the flexible docking requires plenty of computing resources and, regardless, acquiring a decent level of enrichment typically demands further rescoring or post-processing. Negative image-based screening is a rigid docking technique that is ultrafast and computationally light but also effective as proven by vast benchmarking and screening experiments. In the NIB screening, the target protein cavity's shape/electrostatics is aligned and compared against ab initio-generated ligand 3D conformers. In this chapter, the NIB methodology is explained at the practical level and both its weaknesses and strengths are discussed candidly.

Negative Image-Based Rescoring: Using Cavity Information to Improve Docking Screening.

Molecular docking produces often lackluster results in real-life virtual screening assays that aim to discover novel drug candidates or hit compounds. The problem lies in the inability of the default docking scoring to properly estimate the Gibbs free energy of binding, which impairs the recognition of the best binding poses and the separation of active ligands from inactive compounds. Negative image-based rescoring (R-NiB) provides both effective and efficient way for re-ranking the outputted flexible docking poses to improve the virtual screening yield. Importantly, R-NiB has been shown to work with multiple genuine drug targets and six popular docking algorithms using demanding benchmark test sets. The effectiveness of the R-NiB methodology relies on the shape/electrostatics similarity between the target protein's ligand-binding cavity and the docked ligand poses. In this chapter, the R-NiB method is described with practical usability in mind.

Screening of Natural Products Targeting SARS-CoV-2-ACE2 Receptor Interface - A MixMD Based HTVS Pipeline.

The COVID-19 pandemic, caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a severe global health crisis now. SARS-CoV-2 utilizes its Spike protein receptor-binding domain (S-protein) to invade human cell through binding to Angiotensin-Converting Enzyme 2 receptor (ACE2). S-protein is the key target for many therapeutics and vaccines. Potential S-protein-ACE2 fusion inhibitor is expected to block the virus entry into the host cell. In many countries, traditional practices, based on natural products (NPs) have been in use to slow down COVID-19 infection. In this study, a protocol was applied that combines mixed solvent molecular dynamics simulations (MixMD) with high-throughput virtual screening (HTVS) to search NPs to block SARS-CoV-2 entry into the human cell. MixMD simulations were employed to discover the most promising stable binding conformations of drug-like probes in the S-protein-ACE2 interface. Detected stable sites were used for HTVs of 612093 NPs to identify molecules that could interfere with the S-protein-ACE2 interaction. In total, 19 NPs were selected with rescoring model. These top-ranked NP-S-protein complexes were subjected to classical MD simulations for 300 ns (3 replicates of 100 ns) to estimate the stability and affinity of binding. Three compounds, ZINC000002128789, ZINC000002159944 and SN00059335, showed better stability in all MD runs, of which ZINC000002128789 was predicted to have the highest binding affinity, suggesting that it could be effective modulator in RBD-ACE2 interface to prevent SARS-CoV-2 infection. Our results support that NPs may provide tools to fight COVID-19.

In vitro glucuronidation of 7-hydroxycoumarin derivatives in intestine and liver microsomes of Beagle dogs.

Beagle dog is a standard animal model for evaluating nonclinical pharmacokinetics of new drug candidates. Glucuronidation in intestine and liver is an important first-pass drug metabolic pathway, especially for phenolic compounds. This study evaluated the glucuronidation characteristics of several 7-hydroxycoumarin derivatives in beagle dog's intestine and liver in vitro. To this end, glucuronidation rates of 7-hydroxycoumarin (compound 1), 7-hydroxy-4-trifluoromethylcoumarin (2), 6-methoxy-7-hydroxycoumarin (3), 7-hydroxy-3-(4-tolyl)coumarin (4), 3-(4-fluorophenyl)coumarin (5), 7-hydroxy-3-(4-hydroxyphenyl)coumarin (6), 7-hydroxy-3-(4-methoxyphenyl)coumarin (7), and 7-hydroxy-3-(1H-1,2,4-tirazole)coumarin (8) were determined in dog's intestine and liver microsomes, as well as recombinant dog UGT1A enzymes. The glucuronidation rates of 1, 2 and 3 were 3-10 times higher in liver than in small intestine microsomes, whereas glucuronidation rates of 5, 6, 7 and 8 were similar in microsomes from both tissues. In the colon, glucuronidation of 1 and 2 was 3-5 times faster than in small intestine. dUGT1A11 glucuronidated efficiently all the substrates and was more efficient catalyst for 8 than any other dUGT1A. Other active enzymes were dUGT1A2 that glucuronidated efficiently 2, 3, 4, 5, 6 and 7, while dUGT1A10 glucuronidated efficiently 1, 2, 3, 4, 5 and 7. Kinetic analyses revealed that the compounds' Km values varied between 1.1 (dUGT1A10 and 2) and 250 µM (dUGT1A7 and 4). The results further strengthen the concept that dog intestine has high capacity for glucuronidation, and that different dUGT1As mediate glucuronidation with distinct substrates selectivity in dog and human.

Getting Docking into Shape Using Negative Image-Based Rescoring.

The failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in structure-based drug discovery. To remedy this problem, elaborate rescoring and postprocessing schemes have been developed with a varying degree of success, specificity, and cost. The negative image-based rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets. The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein's ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is directly used in the similarity comparison to rank the explicit docking poses. Here, the PANTHER/ShaEP-based R-NiB methodology is tested with six popular docking softwares, including GLIDE, PLANTS, GOLD, DOCK, AUTODOCK, and AUTODOCK VINA, using five validated benchmark sets. Overall, the results indicate that R-NiB outperforms the default docking scoring consistently and inexpensively, demonstrating that the methodology is ready for wide-scale virtual screening usage.

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.

A Practical Perspective: The Effect of Ligand Conformers on the Negative Image-Based Screening.

Negative image-based (NIB) screening is a rigid molecular docking methodology that can also be employed in docking rescoring. During the NIB screening, a negative image is generated based on the target protein's ligand-binding cavity by inverting its shape and electrostatics. The resulting NIB model is a drug-like entity or pseudo-ligand that is compared directly against ligand 3D conformers, as is done with a template compound in the ligand-based screening. This cavity-based rigid docking has been demonstrated to work with genuine drug targets in both benchmark testing and drug candidate/lead discovery. Firstly, the study explores in-depth the applicability of different ligand 3D conformer generation software for acquiring the best NIB screening results using cyclooxygenase-2 (COX-2) as the example system. Secondly, the entire NIB workflow from the protein structure preparation, model build-up, and ligand conformer generation to the similarity comparison is performed for COX-2. Accordingly, hands-on instructions are provided on how to employ the NIB methodology from start to finish, both with the rigid docking and docking rescoring using noncommercial software. The practical aspects of the NIB methodology, especially the effect of ligand conformers, are discussed thoroughly, thus, making the methodology accessible for new users.

Development of new Coumarin-based profluorescent substrates for human cytochrome P450 enzymes.

Cytochrome P450 (CYP) enzymes constitute an essential xenobiotic metabolizing system that regulates the elimination of lipophilic compounds from the body. Convenient and affordable assays for CYP enzymes are important for assessing these metabolic pathways. In this study, 10 novel profluorescent coumarin derivatives with various substitutions at carbons 3, 6 and 7 were developed. Molecular modeling indicated that 3-phenylcoumarin offers an excellent scaffold for the development of selective substrate compounds for various human CYP forms, as they could be metabolized to fluorescent 7-hydroxycoumarin derivatives. Oxidation of profluorescent coumarin derivatives to fluorescent metabolites by 13 important human liver xenobiotic-metabolizing CYP forms was determined by enzyme kinetic assays. Four of the coumarin derivatives were converted to fluorescent metabolites by CYP1 family enzymes, with 6-methoxy-3-(4-trifluoromethylphenyl)coumarin being oxidized selectively by CYP1A2 in human liver microsomes. Another set of four compounds were metabolized by CYP2A6 and CYP1 enzymes. 7-Methoxy-3-(3-methoxyphenyl)coumarin was oxidized efficiently by CYP2C19 and CYP2D6 in a non-selective fashion. The advantages of the novel substrates were (1) an excellent signal-to-background ratio, (2) selectivity for CYP1 forms, and (3) convenient multiwell plate measurement, allowing for precise determination of potential inhibitors of important human hepatic forms CYP1A2, CYP2C19 and CYP2D6.

Suitability of MMGBSA for the selection of correct ligand binding modes from docking results.

The estimation of the correct binding mode and affinity of a ligand into a target protein using computational methods is challenging. However, docking can introduce poses from which the correct binding mode could be identified using other methods. Here, we analyzed the reliability of binding energy estimation using the molecular mechanics-generalized Born surface area (MMGBSA) method without and with energy minimization to identify the likely ligand binding modes within docking results. MMGBSA workflow (a) outperformed docking in recognizing the correct binding modes of androgen receptor ligands and (b) improved the correlation coefficient of computational and experimental results of rescored docking poses to phosphodiesterase 4B. Combined with stability and atomic distance analysis, MMGBSA helped to (c) identify the binding modes and sites of metabolism of cytochrome P450 2A6 substrates. The standard deviation of estimated binding energy within one simulation was lowered by minimization in all three example cases. Minimization improved the identification of the correct binding modes of androgen receptor ligands. Although only three case studies are shown, the results are analogous and indicate that these behaviors could be generalized. Such identified binding modes could be further used, for example, with free energy perturbation methods to understand binding energetics more accurately.