Accelerating AutoDock Vina with GPUs.

Bioinformatics can be defined as the use of computational tools in the study of problems and biological issues, covering applications related to several areas of knowledge, such as biochemistry and computer science. Thus, virtual screening acts in a standard step before laboratory experiments, for the discovery of new drugs. This technique involves calculating the estimated affinities (molecular docking) and the plausible binding modes of many drug candidates. The main focus of this work is the use of AutoDock Vina, a tool for molecular docking and virtual screening, as well as a distributed implementation using the Python programming language. For the configuration of the experiments VS Framework was used, this is a tool of preparation of virtual screening in which it allows the user to configure in a simplified way an experiment through a web platform. RPyC (Remote Python Call), a Python library for symmetric remote procedure calls was used to execute jobs. Basically, RPyC uses the Remote Procedure Call (RPC) protocol for a client-server implementation. The distributed virtual screening architecture proposed in this work is defined in 5 steps: Step 1 (configuration of the experiment), the VS Framework is used for the initial configuration of the experiment, with the other steps are automated by a Python script; Step 2 (preparing files), all virtual screening entries are converted to the default formats of the rest of the run, for which AutoDock Tools is used; Step 3 (upload the files), the converted files are submitted to the computers that integrate the distributed execution; Step 4 (perform virtual screening) and Step 5 (download the results), the virtual screening is processed and the results are sent to the computer that started the experiment. The results were listed among four combinations of simplified distributed computing infrastructures, divided by 5 virtual machines. The processing of each receptor-ligand in a distributed way had a better performance with the increase of computers in the structure. Finally, the analysis showed that these platforms are suitable for virtual screening executions with different receptor/ligand sizes. These considerations may guide scientists in choosing the best computing framework for their large-scale experiments.

Papain-like protease (PLpro) and 3-chymotrypsin-like protease (3CLpro or Mpro) are viral enzymes essential for the replication of SARS-CoV-2, the virus causing coronavirus disease of 2019 (COVID-19) infection. While 3CLpro has been the main target of many potential antivirals including nirmatrelvir (active ingredient of Paxlovid), PLpro has proven to be more challenging to target and only a handful of inhibitors have been disclosed. However, PLpro inhibitors would enrich the therapeutic arsenal against COVID-19 resistant strains to 3CLpro inhibitors and in future coronavirus pandemics. Combining our experience with 3CLpro covalent inhibitors with our expertise in structure-based covalent drug discovery, we rationally designed PLpro inhibitors achieving a maximum potency (IC50) of 13 μM through fusion of GRL0617 and VIR-251 PLpro inhibitors. In parallel, we launched an integrated large scale virtual screening/experimental approach, identifying four novel chemical series active at micromolar concentrations against PLpro. We report herein our investigations including rational design, virtual screening, synthesis of selected structures and in vitro assays leading to novel PLpro inhibitors. Surprisingly, these two very distinct approaches led to structures with similar features.

Virtual screening is a new approach attracting increasing levels of interest in the pharmaceutical industry, as a productive and cost-effective technology in the search for novel lead compounds. The preparation of millions of small molecular compounds is the prerequisite for large-scale virtual screening, and these massive data are usually provided with different format. In addition, scientists often need to select some of them that meet certain conditions. Therefore, an efficient data management approach is playing an important role in virtual screening process for managing large-scale small molecular compounds. In this paper, we represent a comprehensive data management framework for pre-docking in large-scale virtual screening. In this framework, we construct a distributed chemical database and utilize parallel processing approach to search certain molecules from the database on the scale of at least several million. We also develop a proxy schema, which is responsible to perform the basic function (such as, splitting large-scale data, update, insert and so on) a collection of multiple, logically interrelated databases distributed over a computer network, meanwhile, we design and establish a rule of splitting large-scale data with optimization. Finally, we simulate and demonstrate a stress test of constructing and searching database. It turns out that our proposal could make the preparing phase of virtual screening process more simple and efficient.

Virtual high-throughput screening (vHTS) is an invaluable method in modern drug discovery. It permits screening large datasets or databases of chemical structures for those structures binding possibly to a drug target. Virtual screening is typically performed by docking code, which often runs sequentially. Processing of huge vHTS datasets can be parallelized by chunking the data because individual docking runs are independent of each other. The goal of this work is to find an optimal splitting maximizing the speedup while considering overhead and available cores on Distributed Computing Infrastructures (DCIs). We have conducted thorough performance studies accounting not only for the runtime of the docking itself, but also for structure preparation. Performance studies were conducted via the workflow-enabled science gateway MoSGrid (Molecular Simulation Grid). As input we used benchmark datasets for protein kinases. Our performance studies show that docking workflows can be made to scale almost linearly up to 500 concurrent processes distributed even over large DCIs, thus accelerating vHTS campaigns significantly.

The ever-evolving technological world is undergoing a paradigm shift. After the older generations of the mainframe, the PC, the client-server architectures and the web, the fifth generation of computing now ushers itself in. Cloud computing has become the buzzword of the day and is being hailed by many as the technology of tomorrow. But despite its wide popularity and partial implementation and adoption in many companies with an avalanche of gray literature available, the concept of 'cloud computing' remains considerably shroud amidst ambiguity and vagueness. Almost every other article on cloud computing seems to have its own conception of the cloud. The first half of this paper tries to analyze the reason behind this situation. The second and main part of this paper focuses on the extended concept of the 'personal cloud'. Cloud computing is, no doubt, an excellent paradigm for enterprise computing but this paper considers the other side of the equation -- personal computing. Personal cloud computing services on a commercial basis abound on the web but in this paper we consider in depth whether cloud computing is really all that compatible to personal computing, if at all.

Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set () or the test set (e.g., specificity, selectivity or ). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.

Using self-organizing maps to accelerate similarity search

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

A high throughput framework for virtual screening (VS) applications has been designed which enables researchers in in-silico drug discovery to deploy large scale time-consuming virtual screening experiments on High Performance/Grid computing environments. Issues related to enabling virtual screening on grids have been addressed extensively in the past; in this paper we describe a new framework, which supports the rapid deployment of virtual screening services based on UNICORE-6 and Meta Scheduling Service (MSS). We demonstrate, how the deployment of complex virtual screening workflows is facilitated by this framework and we provide some first results of the virtual laboratory testbed established in the course of the EU project PHOSPHORUS. The flexibility of the new VS framework allows easy deployment and integration of new resources and applications, making the Grid more accessible to the users.

Virtual screening is a widely used strategy in modern drug discovery and 2D fingerprint similarity is an important tool that has been successfully applied to retrieve active compounds from large datasets. However, it is not always straightforward to select appropriate fingerprint method and associated settings for a given problem. Here, we applied eight different fingerprint methods, as implemented in the new cheminformatics package Canvas, on a well validated dataset covering five targets. The fingerprint methods include Linear, Dendritic, Radial, MACCS, MOLPRINT2D, Pairwise, Triplet, and Torsion. We find that most fingerprints have similar retrieval rates on average; however, each has special characteristics that distinguish its performance on different query molecules and ligand sets. For example, some fingerprints exhibit a significant ligand size dependency whereas others are more robust with respect to variations in the query or active compounds. In cases where little information is known about the active ligands, MOLPRINT2D fingerprints produce the highest average retrieval actives. When multiple queries are available, we find that a fingerprint averaged over all query molecules is generally superior to fingerprints derived from single queries. Finally, a complementarity metric is proposed to determine which fingerprint methods can be combined to improve screening results. A more systematic virtual screening study has also been conducted to investigate the interrelation between eight fingerprinting methods, eleven atomtyping schemes, seven bit scaling rules, and four similarity metrics. In total, 24,068 virtual screens were performed to assess the effectiveness of each combination of options to identify active ligands in a database screen performed on 11 pharmaceutically relevant targets. Significant variations in enrichments were observed with all explored parameters. In general, fingerprints such as MOLPRINT2D and Dendritic that contain information about local environment beyond simple linear paths outperformed other fingerprint methods. Atomtyping schemes with more specific information were generally superior to more generic atomtyping schemes. With the best identified settings, enrichment factors across all targets could be improved considerably. No single combination of settings performed optimally on all targets and therefore we provide recommendations to improve enrichments based on different requirements.

Virtual screening is becoming an important tool for drug discovery. However, the application of virtual screening has been limited by the lack of accurate scoring functions. Here, we present a novel scoring function, MedusaScore, for evaluating protein-ligand binding. MedusaScore is based on models of physical interactions that include van der Waals, solvation, and hydrogen bonding energies. To ensure the best transferability of the scoring function, we do not use any protein-ligand experimental data for parameter training. We then test the MedusaScore for docking decoy recognition and binding affinity prediction and find superior performance compared to other widely used scoring functions. Statistical analysis indicates that one source of inaccuracy of MedusaScore may arise from the unaccounted entropic loss upon ligand binding, which suggests avenues of approach for further MedusaScore improvement.

The holistic integration of virtual screening in drug discovery

Large-scale computing technologies have enabled high-throughput virtual screening involving thousands to millions of drug candidates. It is not trivial, however, for biochemical scientists to evaluate the technical alternatives and their implications for running such large experiments. Besides experience with the molecular docking tool itself, the scientist needs to learn how to run it on high-performance computing (HPC) infrastructures, and understand the impact of the choices made. Here, we review such considerations for a specific tool, AutoDock Vina, and use experimental data to illustrate the following points: (1) an additional level of parallelization increases virtual screening throughput on a multi-core machine; (2) capturing of the random seed is not enough (though necessary) for reproducibility on heterogeneous distributed computing systems; (3) the overall time spent on the screening of a ligand library can be improved by analysis of factors affecting execution time per ligand, including number of active torsions, heavy atoms and exhaustiveness. We also illustrate differences among four common HPC infrastructures: grid, Hadoop, small cluster and multi-core (virtual machine on the cloud). Our analysis shows that these platforms are suitable for screening experiments of different sizes. These considerations can guide scientists when choosing the best computing platform and set-up for their future large virtual screening experiments.Electronic supplementary materialThe online version of this article (doi:10.1007/s10822-016-9900-9) contains supplementary material, which is available to authorized users.

Several in-house developed descriptors and our in-house docking tool ActDock were compared with virtual screening on the data set of useful decoys (DUD). The results were compared with the chemical fingerprint descriptor from ChemAxon and with the docking results of the original DUD publication. The DUD is the first published data set providing active molecules, decoys, and references for crystal structures of ligand-target complexes. The DUD was designed for the purpose of evaluating docking programs. It contains 2950 active compounds against a total of 40 target proteins. Furthermore, for every ligand the data set contains 36 structurally dissimilar decoy compounds with similar physicochemical properties. We extracted the ligands from the target proteins to extend the applicability of the data set to include ligand based virtual screening. From the 40 target proteins, 37 contained ligands that we used as query molecules for virtual screening evaluation. With this data set a large comparison was done between four different chemical fingerprints, a topological pharmacophore descriptor, the Flexophore descriptor, and ActDock. The Actelion docking tool relies on a MM2 forcefield and a pharmacophore point interaction statistic for scoring; the details are described in this publication. In terms of enrichment rates the chemical fingerprint descriptors performed better than the Flexophore and the docking tool. After removing molecules chemically similar to the query molecules the Flexophore descriptor outperformed the chemical descriptors and the topological pharmacophore descriptors. With the similarity matrix calculations used in this study it was shown that the Flexophore is well suited to find new chemical entities via "scaffold hopping". The Flexophore descriptor can be explored with a Java applet at http://www.cheminformatics.ch in the submenu Tools-->Flexophore. Its usage is free of charge and does not require registration.

Hormones are vital molecules for human differentiation, development, and health. Among them, the female reproduction, development, and cancer-related hormone progesterone has been cast interests for its involvement in nearly all aspects of female health. Recently researches have demonstrated that progesterone, in alliance with estrogen, would suppress breast cancer cell proliferation in cell lines and mouse models. Its receptor, the progesterone receptor (PR), which is a member of the nuclear receptor superfamily, has gradually been recognized as a potential drug target. The dysfunction of this PR would contribute to different types of cancers, including breast cancer. Although there have been several drug molecules designed or discovered against this PR, the side-effects of these drugs are not to be ignored. The necessity of synthesizing new types of drugs against PR, especially its ligand binding domain (LBD), is never decreased. Current studies about PR mainly focus on the biological functions, signaling pathway, its involvement in cancer models. The structural and dynamics of PR, which lay the foundation of the biological understandings, however, are limited, partially due to the difficulty of performing X-ray crystallography or nuclear magnetic resonance spectroscopy (NMR) of the PR, or its domains. We hope to incorporate the structure and dynamics information of PR, or more specifically, the LBD of PR, to identify high potential PR lead molecules for pharmaceutical purposes. Therefore, starting from the available experimental solved structures, we applied biophysics methods to 1) explore the dynamics of PR LBD, 2) and understand the interactions between PR LBD and its agonists and antagonists, 3) and screen large compound libraries to identify potential lead-like molecules. Molecular dynamics (MD) simulations and advanced sampling methods generate good models to estimate the energy landscape of the apo-form PR LBD. The results suggest that the agonistic PR LBD conformation is a meta-stable state. Several other meta-stable states have also been identified and they could be adopted for further virtual screening studies. And the ligand binding (either an agonist or an antagonist) would induce the LBD conformational adaptations more towards the “closed” agonistic state, though different ligands may induce different PR LBD dynamics. We also found that the co-repressor peptide is a necessary component for maintaining the antagonistic conformation. In meanwhile, the ligand-induced conformational adaptations result from both hydrophobic and electrostatic forces. Subsequently, large-scale virtual screening (VS), as well as machine learning-based rescoring calculations, were performed. Towards PR LBD VS, we constructed highly accurate and low false positive rate models and identified 21 potential lead-like molecules, which would be further tested in future. The computational methods or models we used here to fill in the void in structural and dynamics knowledge of PR. The identified molecules could be potential lead molecules for further analysis. The drug discovery strategy we applied would also be useful and could be applied to other targets.