Rigorous Free Energy Simulations in Virtual Screening.

Rigorous physics-based methods to calculate binding free energies of protein-ligand complexes have become a valued component of structure-based drug design. Relative and absolute binding free energy calculations have been deployed prospectively in support of solving diverse drug discovery challenges. Here we review recent applications of binding free energy calculations to fragment growing and linking, scaffold hopping, binding pose validation, virtual screening, covalent enzyme inhibition, and positional analogue scanning. Furthermore, we discuss the merits of using protein models and highlight recent efforts to replace costly binding free energy calculations with predictions from machine learning models trained on a limited number of free energy perturbation or thermodynamic integration calculations thereby allowing for extended chemical space exploration.

The holistic integration of virtual screening in drug discovery

Alchemical binding free energy (BFE) calculations offer an efficient and thermodynamically rigorous approach to in silico binding affinity predictions. As a result of decades of methodological improvements and recent advances in computer technology, alchemical BFE calculations are now widely used in drug discovery research. They help guide the prioritization of candidate drug molecules by predicting their binding affinities for a biomolecular target of interest (and potentially selectivity against undesirable antitargets). Statistical variance associated with such calculations, however, may undermine the reliability of their predictions, introducing uncertainty both in ranking candidate molecules and in benchmarking their predictive accuracy. Here, we present a computational method that substantially improves the statistical precision in BFE calculations for a set of ligands binding to a common receptor by dynamically allocating computational resources to different BFE calculations according to an optimality objective established in a previous work from our group and extended in this work. Our method, termed Network Binding Free Energy (NetBFE), performs adaptive BFE calculations in iterations, re-optimizing the allocations in each iteration based on the statistical variances estimated from previous iterations. Using examples of NetBFE calculations for protein binding of congeneric ligand series, we demonstrate that NetBFE approaches the optimal allocation in a small number (≤5) of iterations and that NetBFE reduces the statistical variance in the BFE estimates by approximately a factor of 2 when compared to a previously published and widely used allocation method at the same total computational cost.

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.

We investigate the role of order/disorder transitions in alchemical simulations of protein-ligand absolute binding free energies. We show, in the context of a potential of mean force description, that for a benchmarking system (the complex of the L99A mutant of T4 lysozyme with 3-iodotoluene) and for a more challenging system relevant for medicinal applications (the complex of the farnesoid X receptor with inhibitor 26 from a recent D3R challenge) that order/disorder transitions can significantly hamper Hamiltonian replica exchange sampling efficiency and slow down the rate of equilibration of binding free energy estimates. We further show that our analytical model of alchemical binding combined with the formalism developed by Straub et al. for the treatment of order/disorder transitions of molecular systems can be successfully employed to analyze the transitions and help design alchemical schedules and soft-core functions that avoid or reduce the adverse effects of rare binding/unbinding transitions. The results of this work pave the way for the application of these techniques to the alchemical estimation with explicit solvation of hydration free energies and absolute binding free energies of systems undergoing order/disorder transitions.

We assess costs and efficiency of state-of-the-art high-performance cloud computing and compare the results to traditional on-premises compute clusters. Our use case is atomistic simulations carried out with the GROMACS molecular dynamics (MD) toolkit with a particular focus on alchemical protein–ligand binding free energy calculations. We set up a compute cluster in the Amazon Web Services (AWS) cloud that incorporates various different instances with Intel, AMD, and ARM CPUs, some with GPU acceleration. Using representative biomolecular simulation systems, we benchmark how GROMACS performs on individual instances and across multiple instances. Thereby we assess which instances deliver the highest performance and which are the most cost-efficient ones for our use case. We find that, in terms of total costs, including hardware, personnel, room, energy, and cooling, producing MD trajectories in the cloud can be about as cost-efficient as an on-premises cluster given that optimal cloud instances are chosen. Further, we find that high-throughput ligand-screening can be accelerated dramatically by using global cloud resources. For a ligand screening study consisting of 19 872 independent simulations or ∼200 μs of combined simulation trajectory, we made use of diverse hardware available in the cloud at the time of the study. The computations scaled-up to reach peak performance using more than 4 000 instances, 140 000 cores, and 3 000 GPUs simultaneously. Our simulation ensemble finished in about 2 days in the cloud, while weeks would be required to complete the task on a typical on-premises cluster consisting of several hundred nodes.

In the hit identification stage of drug discovery, a diverse chemical space needs to be explored to identify initial hits. Contrary to empirical scoring functions, absolute protein-ligand binding free-energy perturbation (ABFEP) provides a theoretically more rigorous and accurate description of protein-ligand binding thermodynamics and could, in principle, greatly improve the hit rates in virtual screening. In this work, we describe an implementation of an accurate and reliable ABFEP method in FEP+. We validated the ABFEP method on eight congeneric compound series binding to eight protein receptors including both neutral and charged ligands. For ligands with net charges, the alchemical ion approach is adopted to avoid artifacts in electrostatic potential energy calculations. The calculated binding free energies correlate with experimental results with a weighted average of R2 = 0.55 for the entire dataset. We also observe an overall root-mean-square error (RMSE) of 1.1 kcal/mol after shifting the zero-point of the simulation data to match the average experimental values. Through ABFEP calculations using apo versus holo protein structures, we demonstrated that the protein conformational and protonation state changes between the apo and holo proteins are the main physical factors contributing to the protein reorganization free energy manifested by the overestimation of raw ABFEP calculated binding free energies using the holo structures of the proteins. Furthermore, we performed ABFEP calculations in three virtual screening applications for hit enrichment. ABFEP greatly improves the hit rates as compared to docking scores or other methods like metadynamics. The good performance of ABFEP in rank ordering compounds demonstrated in this work confirms it as a useful tool to improve the hit rates in virtual screening, thus facilitating hit discovery.

Identifying the novel and potential starting lead compounds remains major challenge in drug discovery industry. In 90’s, high-throughput screening is a common practice in early stage of a project; screening of large number compounds to identify potential starting lead compounds which can modulate target of their interest. Although high-throughput screening has some remarkable successes in identifying potential drug leads, still HTVS is not fruitful as expected, due to the costly nature of chemicals, assays and also depends on the chemical space of the input library [1-4]. Over the years, tremendous research has carried out towards the designing of virtual libraries and virtual screening to identifying the potential starting leads, to reduce the cost of High throughput Screening and to improve the success rate. In current issue of “Current Topics in Medicinal Chemistry (CTMC)”, we focused on the recent trends in library design, Denovo studies, structure and ligand-based virtual screening approaches towards the medicinal chemistry and drug discovery topics and entitled the special topic with “Recent Trends in Library Design and Virtual Screening in Medicinal Chemistry and Drug Discovery”. Articles in this special issue have been contributed by experts from many parts of the world. In the first article, Drs. Rahul and Se Won Park discussed about discovery of a tuberculosis drug: bedaquiline, its anti-tuberculosis effects, mode of action and also discussed about computer-aided drug design approach to predict the binding mode for bedaquiline. In the next article, Drs. Prema Latha & Thomas discussed about successful application of structure-based drug design of Zanamivir (Relenza™) and oseltamivir (Tamiflu ® ), antiviral drugs. Also, discussed about application of computer-aided methods in identifying leads against influenza targets. Next article contributed by Drs. Harikishore & Yoon discusses about recent developments in denovo drug design and also explained successful application of the ligand and receptor based de novo drug design approaches. In the next article, Drs. Rodolpho & Carolina, summarizes the recent developments in virtual screening strategies and also highlights the recent achievements and as well as challenges. Also, discussed recent example of successful application for the identification of novel hit compounds for Trypanosoma cruzi sterol 14 α-demethylase (CYP51). In another article, Drs. Dimitar, Girinath, & Mati has covered a variety of studies have demonstrated the potential of machine- learning methods for predicting compounds as potential drug candidates. Also, provided an overview of the strategies and current progress in using machine learning methods for drug design and the potential of the respective model development tools. In next article, Drs. Evanthia & Zoe Cournia has covered an overview to the principles and applications of Structure-based Virtual Screening (VS) approaches and discussed on recent trends in library design, and as well as discuss limitations of the method.

With the emergence of SARS-CoV-2 mutant strains, especially the epidemic of Omicron, it continues to evolve to strengthen immune evasion. Omicron BQ. 1 and XBB pose a serious threat to the current COVID-19 vaccine (including bivalent mRNA vaccine for mutant strains) and COVID-19-positive survivors, and all current therapeutic monoclonal antibodies are ineffective against them. Older people, those with multimorbidity, and those with specific underlying health conditions remain at increased risk of COVID-19 hospitalization and death after the initial vaccine booster. However, small-molecule drugs for conserved targets remain effective and urgently needed. The non-structural protein of SARS-CoV-2 non-structural protein 1(Nsp1) can bind to the host 40S ribosomal subunit and activate the nuclease to hydrolyze the host RNA, while the viral RNA is unaffected, thus hijacking the host system. First, the present study analyzed mutations in the Nsp1 protein and then constructed a maximum-likelihood phylogenetic tree. A virtual drug screening method based on the Nsp1 structure (Protein Data Bank ID: 7K5I) was constructed, 7495 compounds from three databases were collected for molecular docking and virtual screening, and the binding free energy was calculated by the MM/GBSA method. Our study shows that Nsp1 is relatively conserved and can be used as a comparatively fixed drug target and that therapies against Nsp1 will target all of these variants. Golvatinib, Gliquidone, and Dihydroergotamine were superior to other compounds in the crystal structure of binding conformation and free energy. All effectively interfered with Nsp1 binding to 40S protein, confirming the potential inhibitory effect of these three compounds on SARS-CoV-2. In particular, Golwatinib provides a candidate for treatment and prophylaxis in elderly patients with Omicjon, suggesting further evaluation of the anti-SARS-CoV-2 activity of these compounds in cell culture. Further studies are needed to determine the utility of this finding through prospective clinical trials and identify other meaningful drug combinations.

We propose a free energy calculation method for receptor-ligand binding, which have multiple binding poses that avoids exhaustive enumeration of the poses. For systems with multiple binding poses, the standard procedure is to enumerate orientations of the binding poses, restrain the ligand to each orientation, and then, calculate the binding free energies for each binding pose. In this study, we modify a part of the thermodynamic cycle in order to sample a broader conformational space of the ligand in the binding site. This modification leads to more accurate free energy calculation without performing separate free energy simulations for each binding pose. We applied our modification to simple model host-guest systems as a test, which have only two binding poses, by using a single decoupling method (SDM) in implicit solvent. The results showed that the binding free energies obtained from our method without knowing the two binding poses were in good agreement with the benchmark results obtained by explicit enumeration of the binding poses. Our method is applicable to other alchemical binding free energy calculation methods such as the double decoupling method (DDM) in explicit solvent. We performed a calculation for a protein-ligand system with explicit solvent using our modified thermodynamic path. The results of the free energy simulation along our modified path were in good agreement with the results of conventional DDM, which requires a separate binding free energy calculation for each of the binding poses of the example of phenol binding to T4 lysozyme in explicit solvent. © 2019 Wiley Periodicals, Inc.

High-throughput and virtual screening are important components of modern drug discovery research. Typically, these screening technologies are considered distinct approaches, as one is experimental and the other is theoretical in nature. However, given their similar tasks and goals, these approaches are much more complementary to each other than often thought. Various statistical, informatics and filtering methods have recently been introduced to foster the integration of experimental and in silico screening and maximize their output in drug discovery. Although many of these ideas and efforts have not yet proceeded much beyond the conceptual level, there are several success stories and good indications that early-stage drug discovery will benefit greatly from a more unified and knowledge-based approach to biological screening, despite the many technical advances towards even higher throughput that are made in the screening arena.

The virtual screening (VS) is an important tool used in the modern drug discovery process to identify new leads and drug-like molecules for therapeutic interventions. With the rapid advancements in the computational hardware supported by advanced algorithmic progress and comprehensive data, the VS has gained fast momentum in drug discovery paradigm. The VS protocol is performed predominantly by using the ligand-based (LB) and structure-based (SB) VS methods with each one having its own merits and demerits. The LBVS method works on the similarity approach based on the physicochemical parameters, chemical functionality, and shape similarity of the ligands. The SBVS method works on the complementarity of the ligand with the target binding site and is more preferred than LBVS due to the consideration of both ligand and target information. Further in the SBVS, the available conformational information of the active chemical space helps in making reasonable decisions for the ligand selection. However, the limitations in scoring functions and incorrect pose prediction may result in the inaccurate SB models of limited performance in VS experiments. Various factors interplay in the development of successful VS models and the fine tuning of these factors leads to the efficient VS models with high sensitivity. In this chapter, some of the recent ligand- and structure-based approaches for virtual screening in drug discovery are described.

Identification of HIV-1 reverse transcriptase dual inhibitors by a combined shape-, 2D-fingerprint- and pharmacophore-based virtual screening approach

Cancer continues to remain a disease of scientific concern. Significant interest in targeting the Polo-Box-Domain (PBD) of Polo-like-kinase-1 (PLK-1) by novel ligands has arisen. The 'cleft' constituted by amino acid residues W414, H538, and K540 is the traditional target of PLK-1-PBD-inhibitors. However, this 'cleft' is merely a small part of the larger 'Y'-shaped cavity present therein. The objective of this study was to discover inhibitors of the PLK-1-PBD precisely directed against its trimodular 'Y'-pocket. High-throughput structure-based virtual screening (SBVS) of more than 5 million ligands against the aforementioned PLK-1 'Y'-pocket was performed. The SBVS hits were successively subjected to pass through various filters: VINA score ranking, toxicity checker, 'Special Criteria'-filtration, holistic tri-modular 'Y'-pocket interaction check, drug-likeness filters, and medicinal chemistry filters. Accordingly, we arrived at a single top ligand, 'SHAZ-i.' The top ligand, 3-{2-[(2-Methyl-2-propanyl)sulfonyl]phenyl}-5-phenyl-1,2-oxazole-4-carboxamide, displayed a robust interaction with the target crevice through 15 amino acid residues, an acceptable ΔG value of -7.8 kcal/mol, and a favorable pharmacokinetic profile with no adverse effects on humans. Hence, 3-{2-[(2-Methyl-2-propanyl)sulfonyl]phenyl}-5-phenyl-1,2-oxazole-4-carboxamide could emerge as a potent PLK-1-PBD inhibitor or might act as a 'seed' molecule for design of future inhibitors with a closely related backbone structure.

Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimized implementations of a virtual screening algorithm targeting novel GPU architectures. This work focuses on the analysis of parallel computation patterns and their mapping onto the target architecture. The first method adopts a traditional approach that spreads the computation for a single molecule across the entire GPU. The second uses a novel batched approach that exploits the parallel architecture of the GPU to evaluate more molecules in parallel. Experimental results showed a different behavior depending on the size of the database to be screened, either reaching a performance plateau sooner or having a more extended initial transient period to achieve a higher throughput (up to 5x), which is more suitable for extreme-scale virtual screening campaigns.