Computer-aided Drug Design Research Articles

The Role of Water Networks in Phosphodiesterase Inhibitor Dissociation and Kinetic Selectivity.

In search of new opportunities to develop Trypanosoma brucei phosphodiesterase B1 (TbrPDEB1) inhibitors that have selectivity over the off-target human PDE4 (hPDE4), different stages of a fragment-growing campaign were studied using a variety of biochemical, structural, thermodynamic, and kinetic binding assays. Remarkable differences in binding kinetics were identified and this kinetic selectivity was explored with computational methods, including molecular dynamics and interaction fingerprint analyses. These studies indicate that a key hydrogen bond between GlnQ.50 and the inhibitors is exposed to a water channel in TbrPDEB1, leading to fast unbinding. This water channel is not present in hPDE4, leading to inhibitors with a longer residence time. The computer-aided drug design protocols were applied to a recently disclosed TbrPDEB1 inhibitor with a different scaffold and our results confirm that shielding this key hydrogen bond through disruption of the water channel represents a viable design strategy to develop more selective inhibitors of TbrPDEB1. Our work shows how computational protocols can be used to understand the contribution of solvent dynamics to inhibitor binding, and our results can be applied in the design of selective inhibitors for homologous PDEs found in related parasites.

Exploring the Impact of Protein Chain Selection in Binding Energy Calculations with DFT.

Calculation of binding free energies between a protein and a ligand are highly desired for computer-aided drug design. Here we approximate the binding energies of ABL1, an enzyme which is the target for drugs used in the treatment of chronic myeloid leukaemia, with minimal models and density functional theory (DFT). Starting from the crystal structures of protein-drug complexes, we estimated the binding free energies having used all available individual molecules (protein chains) within each structure, not only a single one as commonly used, in order to see if the choice of the protein chain is important in such calculations. Differences were observed between chains in the same file. Energy decomposition analysis (EDA) revealed that the most important factors for binding were exchange, repulsion and electrostatics. The desolvation term varied dramatically between the inhibitors (between 4.2 and 92.3 kcal/mol). All functionals showed similar patterns in the EDA and in discriminating between the ligands. Non-covalent interactions (NCI) analysis was used to further explain the differences between protein chains and functionals. Overall, it is shown that small minimal models of a drug binding site can be useful to infer on the suitability of an initial crystal structure for further analysis such as EDA.

SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.

Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into scientific research faces resistance due to myriad challenges: the opaqueness of AI models, the intricate nature of their implementation, and the issue of data scarcity. In response to these impediments, we introduce SmartCADD, an innovative, open-source virtual screening platform that combines deep learning, computer-aided drug design (CADD), and quantum mechanics methodologies within a user-friendly Python framework. SmartCADD is engineered to streamline the construction of comprehensive virtual screening workflows that incorporate a variety of formerly independent techniques─spanning ADMET property predictions, de novo 2D and 3D pharmacophore modeling, molecular docking, to the integration of explainable AI mechanisms. This manuscript highlights the foundational principles, key functionalities, and the unique integrative approach of SmartCADD. Furthermore, we demonstrate its efficacy through a case study focused on the identification of promising lead compounds for HIV inhibition. By democratizing access to advanced AI and quantum mechanics tools, SmartCADD stands as a catalyst for progress in pharmaceutical research and development, heralding a new era of innovation and efficiency.

In Silico Screening of Phytocompounds from West African Traditional Medicine and Molecular Docking Targeting Dengue Virus Protein NS2B/NS3.

Traditional medicine offers a wide range of application for in silico study techniques. This drug research and development strategy is embryonic in the West African context, particularly in Burkina Faso, which is increasingly faced with emerging diseases such as dengue fever. Circulation of the 4 serotypes of this virus has been documented in the country. This study aims to evaluate the therapeutic potential of phytocompounds contained in the West African pharmacopoeia against dengue virus NS2B/NS3 protein, using computational methods integrating several software packages and databases. Based on a literature review, we identified 191 molecules from 30 plants known for their antiviral effects. Five met the inclusion criteria for molecular docking: patulin from calotropis procera, resiniferonol from Euphorbia poissonii, Securinol A from Flueggea virosa, Shikimic acid and Methyl gallate from Terminalia macroptera. The best binding scores were observed between resiniferonol and the serotypes 1, 2 and 4 NS2B/NS3 protease, with binding energies of -7.4 Kcal/mol, -6.8 Kcal/mol and -7.3 Kcal/mol respectively; while the NS2B/NS3 protease of serotype 3 had the best affinity for securinol A (-7 Kcal/mol). This study points the way to further research in computer aided drug design field and calls for multidisciplinary collaboration to promote West African medicinal plants against health challenges.

Pyridazine Derivatives: Molecular Docking, ADMET Prediction, and Synthesis for Antihypertensive Activity.

The drug discovery and development domain has witnessed remarkable advancements due to the integration of computational methods, particularly Computer-Aided Drug Design (CADD). Discovering and creating new drugs involves structural modifications to enhance their effectiveness and physical attributes. This frequently includes employing semisynthetic techniques to investigate structure-activity relationships thoroughly. Noticeable progress in molecular biology, computational chemistry, combinatorial chemistry, and highthroughput screening is steering transformative changes in the pharmaceutical industry. High blood pressure or hypertension, a significant health issue, elevates the chances of heart, kidney, and brain complications, among other health concerns. It's a leading cause of untimely mortality globally. Therefore, it is important to search for new antihypertensive compounds that have fewer side effects and higher therapeutic activity. Following molecular docking of the pyridazine derivatives, compounds were subjected to In-silico ADMET analysis. Subsequently, a low molecular weight compound was synthesized. Among the synthesized compounds characterization procedures include TLC, FT-IR, 1HNMR, and LC-MS techniques. Compound 8 exhibited the most favorable molecular docking results with alpha A1 and beta 1 adrenergic receptors. Compounds 3, 5, and 6 fulfilled the essential ADMET criteria. Subsequently, Compounds 3, 4, and 5 underwent additional synthesis and characterization procedures, including TLC, FT-IR, 1H-NMR, and LC-MS techniques. Similar behavior was observed in compounds 6, 8, 10, and 11, all violating Pfizer's 3/75 rules in terms of TPAS. Hydrazinolysis of these b-benzoyl propionic acids produced pyridazine, which was utilized in synthesizing pyridazine derivatives. TLC, FT-IR, 1HNMR, and LCMS have characterized the compounds.

Unveiling the potential of novel 5α-reductase inhibitors via ligand based drug design, molecular docking and ADME predictions to manage BPH

Benign prostatic hyperplasia (BPH), a prevalent condition among elderly males characterized by the non-cancerous enlargement of the prostate gland is due to the increased stromal and epithelial cell numbers. The enlargement is driven by an active dihydrotestosterone (DHT), synthesized from its prohormone testosterone under the catalytic effect of 5α-reductase Type-II (5-AR2) enzyme. To reduce the increased DHT levels and ultimately to control prostate growth, inhibition of the 5-AR represent an interesting therapeutic target. The 3D crystallography structure 5-AR2, a member of the steroid 5-AR family belonging to the class of oxidoreductase has been resolved recently and opened the door to develop selective novel inhibitors of 5-AR Type-II isoform using molecular modeling techniques. The Force field and Gaussian-field three-dimensional quantitative structure–activity relationship (3D-QSAR) models have been developed using 42 selective 5-AR2 inhibitors belonging to chemical class of steroidal androstene. Partial least squares analysis produced statistically significant model with R2training = 0.9367, 0.9459 and predictability Q2test = 0.7233, 0.8595. Developed 3D-QSAR model include steric, electrostatic, hydrophobic, and hydrogen bond acceptor and donor field indicators, whereas the potential field contributions indicated the importance of steric feature in governing their biological activity. Protein-ligand interaction pattern analysis revealed that amino acid residues GLU57, ARG114, TYR91 present in the active site of 5-AR2 are crucial for lower energy complexes with good binding affinity. Identified hits were finally adjudged for their druggability by determining in-silico pharmacokinetic parameters. Further, RMSD, binding free energy calculations and post MD analysis demonstrated the enhanced binding affinity with better ΔGbind value (-79.6 ± 3.9 kcal/mol) of identified ligand A-10 than the reference molecule finasteride (-69.13 ± 4.69 kcal/mol). Combined approach of ligand-based and structure-based models have provided an improved understanding of the structural properties of androstene class that may be further used to develop novel 5-AR2 inhibitors to be useful in BPH.

Mining Medicinally Relevant Bioreduction Substrates Inspired by Ligand-Based Drug Design.

Exploring the scope of biocatalytic transformations in the absence of enzyme structures without extensive experimentation is a challenging task. To expand the limited substrate capacity of carrot-mediated bioreduction and hunt for new medicinally relevant ketones with minimum cost of labor and time, we deployed a practical method inspired by ligand-based drug design. Through analyzing collected literature data and building pharmacophore and reactivity prediction models, we screened a self-built virtual library of >8000 ketones bearing the most frequently used N,O,S-heterocycles and functional groups in drug discovery. Representative examples were validated, expanding the bioreduction substrate scope. The public availability of our models alongside the straightforward screening workflow makes it time-, labor-, and cost-saving to evaluate unknown bioreduction substrates for medicinal chemistry applications, especially for a large set of structurally differentiated ketones. Our studies also showcase the novelty of utilizing medicinal chemistry principles to solve a general biocatalysis problem.

Discovery of natural rosin-based preservative candidates to control Phytophthora capsici for postharvest disease management of solanaceae vegetables

To mitigate the postharvest harm of pathogens on solanaceous vegetables, a series of natural rosin-1,2,4-triazole derivatives (totally 24) have been designed and developed. In vivo fungicidal activity evaluation showed that Co. 5 f could inhibit the growth of Phytophthora capsici (EC50 = 0.22 mg/L). In vivo activity suggested that Co. 5 f presented certain protective effect on bell pepper (64.93 % – 32.68 %) and tomato (91.93 % – 67.85 %) fruit. Additionally, Co. 5 f prevented declines in both weight and ascorbic acid levels and reduced the malondialdehyde content of bell peppers and tomato, suggesting its efficacy in vegetable preservation during storage. The physiological research revealed that Co. 5 f disrupted the mycelial cell membrane, accelerated the accumulation of reactive oxygen species, and decreased the enzyme activity of peroxidase (POD) and catalase (CAT) protein activities. Subsequently, quantum chemical calculations and molecular docking successfully predicted the active site of Co. 5 f. Metabolomics was then employed to elucidate the mechanism of physiological disorders, suggesting that differential metabolites were primarily enriched in the biosynthesis of amino acid and secondary metabolites. Transcriptomics was also utilized to further uncover the mechanism of mycelial metabolic disorders, revealing that differential genes were primarily enriched in the biosynthesis of secondary metabolites and amino acid. Additionally, the integration of physiology, metabolomics, transcriptomics, and computer-aided drug design unveiled the action mechanism of Co. 5 f against P. capsici, indicating that the regulation of POD and CAT biosynthesis involved six core genes, while the regulation of ARG, HIS, and TRP biosynthesis involved four core genes. Finally, the expression of these ten core genes was validated using quantitative real-time polymerase chain reaction (qRT-PCR). In summary, the above research integrated physiology, metabolomics, transcriptomics and computer-aided drug design to reveal the action mechanism of Co. 5 f, providing a certain theoretical support for the postharvest disease management of bell pepper and tomato fruit.

Hierarchical Virtual Screening of SARS-CoV-2 Main Protease Potential Inhibitors: Similarity Search, Pharmacophore Modeling, and Molecular Docking Study

Background: The outbreak of COVID-19 caused by severe acute respiratory syndrome coronavirus2 (SARS-CoV-2) resulted in a widespread pandemic. Various approaches involved the repositioning of antiviral remedies and other medications. Several therapies, including oral antiviral treatments, represent some approaches to adapting to the long existence of the COVID-19 pandemic. In silico studies provide valuable insights throughout drug discovery and development in compliance with global efforts to overcome the pandemic. The main protease is an essential target in the viral cycle. Computer-aided drug design accelerates the identification of potential treatments, including oral therapy. Aims: This work aims to identify potential SARS-CoV-2 main protease inhibitors using different aspects of in silico approaches. Methods: In this work, we conducted a hierarchical virtual screening of SARS-CoV-2 main protease inhibitors. A similarity search was conducted to screen molecules similar to the inhibitor PF-07321332. Concurrently, structure-based pharmacophores, besides ligand-based pharmacophores, were derived. A drug-likeness filter filtered the compounds retrieved from similarity search and pharmacophore modeling before being subjected to molecular docking. The candidate molecules that showed higher affinity to the main protease than the reference inhibitor were further filtered by absorption, distribution, metabolism, and excretion (ADME) parameters. Results: According to binding affinity and ADME analysis, four molecules (CHEMBL218022, PubChem163362029, PubChem166149100, and PubChem 162396459) were prioritized as promising hits. The compounds above were not reported before; no previous experimental studies and bioactive assays are available. Conclusion: Our time-saving approach represents a strategy for discovering novel SARS-CoV- 2 main protease inhibitors. The ultimate hits may be nominated as leads in discovering novel SARS-CoV-2 main protease inhibitors.

Recent Advances in Nanocarrier-based Approaches to Atopic Dermatitis and Emerging Trends in Drug Development and Design.

Atopic dermatitis (AD), commonly known as Eczema, is a non-communicable skin condition that tends to become chronic. The deteriorating immunological abnormalities are marked by mild to severe erythema, severe itching, and recurrent eczematous lesions. Different pharmacological approaches are used to treat AD. The problem with commercial topical preparations lies in the limitation of skin atrophy, systemic side effects, and burning sensation that decreases patient compliance. The carrier-based system promises to eliminate these shortcomings; thus, a novel approach to treating AD is required. Liposomes, microemulsions, solid lipid nanoparticles (SLNs), nanoemulsions, etc., have been developed recently to address this ailment. Despite extensive research in the development method and various techniques, it has been challenging to demonstrate the commercial feasibility of these carrier- based systems, which illustrates a gap among the different research areas. Further, different soft wares and other tools have proliferated among biochemists as part of a cooperative approach to drug discovery. It is crucial in designing, developing, and analyzing processes in the pharmaceutical industry and is widely used to reduce costs, accelerate the development of biologically innovative active ingredients, and shorten the development time. This review sheds light on the compilation of extensive efforts to combat this disease, the product development processes, commercial products along with patents in this regard, numerous options for each step of computer-aided drug design, including in silico pharmacokinetics, pharmacodynamics, and toxicity screening or predictions that are important in finding the drug-like compounds.

Revolutionizing Drug Discovery: A Comprehensive Review of Computer-Aided Drug Design Approaches

Abstract: Computer-Aided Drug Design (CADD) has significantly advanced the drug discovery process, offering tools to enhance efficiency and reduce costs. This review explores essential CADD methodologies, including molecular docking, virtual screening, ADMET profiling, homology modeling, and Quantitative Structure-Activity Relationship (QSAR) models. Molecular docking predicts interactions between drugs and targets, while virtual screening evaluates large compound libraries to identify promising candidates. ADMET profiling assesses pharmacokinetic and toxicological properties early in development. Homology modeling constructs three-dimensional protein models to aid target identification, and QSAR models predict biological activities based on chemical structures. These integrated approaches streamline drug development, providing a robust framework for modern pharmaceutical research.

Computational approach for drug discovery against Gardnerella vaginalis in quest for safer and effective treatments for bacterial vaginosis

Bacterial vaginosis (BV), primarily attributed to Gardnerella vaginalis, poses significant challenges due to antibiotic resistance and suboptimal treatment outcomes. This study presents an integrated approach to identify potential drug targets and screen compounds against this bacterium by leveraging a computational methodology. Subtractive proteomics of the reference strain ASM286196v1/UMB0386 (assembly accession: GCA_002861965.1) facilitated the prioritization of proteins with essential bacterial functions and pathways as potential drug targets. We selected 3-deoxy-7-phosphoheptulonate synthase (aroG gene product; also known as DAHP synthase) for downstream analysis. Molecular docking was employed in PyRx (AutoDock Vina) to predict binding affinities between aroG inhibitors from the ZINC database and 3-deoxy-7-phosphoheptulonate synthase. Molecular dynamics simulations of 100 ns, using GROMACS, validated the stability of drug-target interactions. Additionally, ADMET profiling aided in the selection of compounds with favorable pharmacokinetic properties and safety profile for human hosts. PBPK profiling showed that ZINC98088375 had the highest bioavailability and efficient systemic circulation. Conversely, ZINC5113880 demonstrated the lowest absorption rate (39.661%). Moreover, cirrhosis, steatosis, and renal impairment appeared to influence blood concentration of the drug, impacting bioavailability. The integrative –omics approach utilized in this study underscores the potential of computer-aided drug design and offers a rational strategy for targeted inhibitor discovery against G. vaginalis. The strategy is an attempt to address the limitations of current BV treatments, including antibiotic resistance, and pave way for the development of safer and more effective therapeutics.

Quantitative structure-activity relationships modelling in antimicrobial peptides design

Antimicrobial peptides are antimicrobial agents with high bioactivity and low bacterial resistance, which inspire scientists to recognize AMPs as appealing candidates for microbial infection treatments. QSAR modeling is one of the most important techniques in computer-aided drug design that is typically beneficial to promoting the design of AMPs. This paper presents an overview about breakthroughs and achievements in AMPs designed by QSAR modeling in recent 10 years, and primarily talks about 6 successful AMPs discoveries covering different QSAR models and solving various antimicrobial problems such as biological activity, cytotoxicity and bacterial resistance, which is responsible for future developments in new generation of AMPs and optimization in QSAR modeling.

4-Hydroxyphenylpyruvate Dioxygenase Inhibitors: From Molecular Design to Synthesis.

Weed resistance is a critical issue in crop production. Among the known herbicides, 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitors are crucial for addressing weed resistance. HPPD inhibitors constitute a pivotal aspect of contemporary crop protection strategies. The advantages of these herbicides are their broad weed spectrum, flexible application, and excellent compatibility with other herbicides. They also exhibit satisfactory crop selectivity and low toxicity and are environmentally friendly. An increasing number of new HPPD inhibitors have been designed by combining computer-aided drug design with conventional design approaches. Herein, the molecular design and structural features of innovative HPPD inhibitors are reviewed to guide the development of new HPPD inhibitors possessing an enhanced biological efficacy.

Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms.

In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.

Rational In Silico Design of Selective TMPRSS6 Peptidomimetic Inhibitors via Exploitation of the S2 Subpocket.

TMPRSS6 is a potential therapeutic target for the treatment of iron overload due to its role in regulating levels of hepcidin. Although potent TMPRSS6 inhibitors have been previously developed, their lack of specificity requires optimization to avoid potential side effects before pursuing preclinical development with in vivo models. Here, using computer-aided drug design based on a TMPRSS6 homology model, we reveal that the S2 position of TMPRSS6 offers a potential avenue to achieve selectivity against other members of the TTSP family. Accordingly, we synthesized novel peptidomimetic molecules containing lipophilic amino acids at the P2 position to exploit this unexplored pocket. This enabled us to identify TMPRSS6-selective small molecules with low nanomolar affinity. Finally, pharmacokinetic parameters were determined, and a compound was found to be potent in cellulo toward its primary target while retaining TTSP-subtype selectivity and showing no signs of alteration in in vitro TEER experiments.

How to Sample Dozens of Substitutions per Site with λ Dynamics.

Alchemical free energy methods are useful in computer-aided drug design and computational protein design because they provide rigorous statistical mechanics-based estimates of free energy differences from molecular dynamics simulations. λ dynamics is a free energy method with the ability to characterize combinatorial chemical spaces spanning thousands of related systems within a single simulation, which gives it a distinct advantage over other alchemical free energy methods that are mostly limited to pairwise comparisons. Recently developed methods have improved the scalability of λ dynamics to perturbations at many sites; however, the size of chemical space that can be explored at each individual site has previously been limited to fewer than ten substituents. As the number of substituents increases, the volume of alchemical space corresponding to nonphysical alchemical intermediates grows exponentially relative to the size corresponding to the physical states of interest. Beyond nine substituents, λ dynamics simulations become lost in an alchemical morass of intermediate states. In this work, we introduce new biasing potentials that circumvent excessive sampling of intermediate states by favoring sampling of physical end points relative to alchemical intermediates. Additionally, we present a more scalable adaptive landscape flattening algorithm for these larger alchemical spaces. Finally, we show that this potential enables more efficient sampling in both protein and drug design test systems with up to 24 substituents per site, enabling, for the first time, simultaneous simulation of all 20 amino acids.

Computer-aided drug design supporting sunscreen research: a showcase study using previously synthesized hybrid UV filter-antioxidant compounds.

Although quantum mechanical calculations have proven effective in accurately predicting UV absorption and assessing the antioxidant potential of compounds, the utilization of computer-aided drug design (CADD) to support sustainable synthesis research of new sunscreen active ingredients remains an area with limited exploration. Furthermore, there are ongoing concerns about the safety and effectiveness of existing sunscreens. Therefore, it remains crucial to investigate photoprotection mechanisms and develop enhanced strategies for mitigating the harmful effects of UVR exposure, improving both the safety and efficacy of sunscreen products. A previous study conducted synthesis research on eight novel hybrid compounds (I-VIII) for use in sunscreen products by molecular hybridization of trans-resveratrol (RESV), avobenzone (AVO), and octinoxate (OMC). Herein, time-dependent density functional theory (TD-DFT) calculations performed in the gas phase on the isolated hybrid compounds (I-VIII) proved to reproduce the experimental UV absorption. Resveratrol-avobenzone structure-based hybrids (I-IV) present absorption maxima in the UVB range with slight differences between them, while resveratrol-OMC structure-based hybrids (V-VIII) showed main absorption in the UVA range. Among RESV-OMC hybrids, compounds V and VI exhibited higher UV absorption intensity, and compound VIII stood out for its broad-spectrum coverage in our simulations. Furthermore, both in silico and in vitro analyses revealed that compounds VII and VIII exhibited the highest antioxidant activity, with compound I emerging as the most reactive antioxidant within RESV-AVO hybrids. The study suggests a preference for the hydrogen atom transfer (HAT) mechanism over single-electron transfer followed by proton transfer (SET-PT) in the gas phase. With a strong focus on sustainability, this approach reduces costs and minimizes effluent production in synthesis research, promoting the eco-friendly development of new sunscreen active ingredients. The SPARTAN'20 program was utilized for the geometry optimization and energy calculations of all compounds. Conformer distribution analysis was performed using the Merck molecular force field 94 (MMFF94), and geometry optimization was carried out using the parametric method 6 (PM6) followed by density functional theory (DFT/B3LYP/6-31G(d)). The antioxidant behavior of the hybrid compounds (I-VIII) was determined using the highest occupied molecular orbital (εHOMO) and the lowest unoccupied molecular orbital (εLUMO) energies, as well as the bond dissociation enthalpy (BDE), ionization potential (IP), and proton dissociation enthalpy (PDE) values, all calculated at the same level of structural optimization. TD-DFT study is carried out to calculate the excitation energy using the B3LYP functional with the 6-31G(d) basis set. The calculated transitions were convoluted with a Gaussian profile using the Gabedit program.

Ligand Binding Affinity Prediction for Membrane Proteins with Alchemical Free Energy Calculation Methods.

Alchemical relative binding free energy (ΔΔG) calculations have shown high accuracy in predicting ligand binding affinity and have been used as important tools in computer-aided drug discovery and design. However, there has been limited research on the application of ΔΔG methods to membrane proteins despite the fact that these proteins represent a significant proportion of drug targets, play crucial roles in biological processes, and are implicated in numerous diseases. In this study, to predict the binding affinity of ligands to G protein-coupled receptors (GPCRs), we employed two ΔΔG calculation methods: thermodynamic integration (TI) with AMBER and the alchemical transfer method (AToM) with OpenMM. We calculated ΔΔG values for 53 transformations involving four class A GPCRs and evaluated the performance of AMBER-TI and AToM-OpenMM. In addition, we conducted tests using different numbers of windows and varying simulation times to achieve reliable ΔΔG results and to optimize resource utilization. Overall, both AMBER-TI and AToM-OpenMM show good agreement with the experimental data. Our results validate the applicability of AMBER-TI and AToM-OpenMM for optimization of lead compounds targeting membrane proteins.

Chemical Probes to Investigate Central Nervous System Disorders: Design, Synthesis and Mechanism of Action of a Potent Human Serine Racemase Inhibitor.

The intricate signaling network within the central nervous system (CNS) involving N-methyl-d-aspartate receptors (NMDARs) has been recognized as a key player in severe neurodegenerative diseases. The indirect modulation of NMDAR-mediated neurotransmission through inhibition of serine racemase (SR)-the enzyme responsible for the synthesis of the NMDAR coagonist d-serine-has been suggested as a therapeutic strategy to treat these conditions. Despite the inherent challenges posed by SR conformational flexibility, a ligand-based drug design strategy has successfully produced a series of potent covalent inhibitors structurally related to amino acid analogues. Among these inhibitors, O-(2-([1,1'-biphenyl]-4-yl)-1-carboxyethyl)hydroxylammonium chloride (28) has emerged as a valuable candidate with a K d of about 5 μM, which makes it one of the most potent hSR inhibitors reported to date. This molecule is expected to inspire the identification of selective hSR inhibitors that might find applications as tools in the study and treatment of several CNS pathologies.