@article{GorgullaDasLeighetal., author = {Gorgulla, Christoph and Das, Krishna M. Padmanabha and Leigh, Kendra E and Cespugli, Marco and Fischer, Patrick D. and Wang, Zi-Fu and Tesseyre, Guilhem and Pandita, Shreya and Shnapir, Alex and Calderaio, Anthony and Hutcheson, Colin and Gechev, Minko and Rose, Alexander and Lewis, Noam and Yaffe, Erez and Luxenburg, Roni and Herce, Henry D. and Durmaz, Vedat and Halazonetis, Thanos D. and Fackeldey, Konstantin and Patten, Justin J. and Chuprina, Alexander and Dziuba, Igor and Plekhova, Alla and Moroz, Yurii and Radchenko, Dmytro and Tarkhanova, Olga and Yavnyuk, Irina and Gruber, Christian C. and Yust, Ryan and Payne, Dave and N{\"a}{\"a}r, Anders M. and Namchuk, Mark N. and Davey, Robert A. and Wagner, Gerhard and Kinney, Jamie and Arthanari, Haribabu}, title = {A Multi-Pronged Approach Targeting SARS-CoV-2 Proteins Using Ultra-Large Virtual Screening}, series = {iScience}, volume = {24}, journal = {iScience}, number = {2}, publisher = {CellPress}, doi = {10.26434/chemrxiv.12682316}, pages = {102021}, abstract = {Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), previously known as 2019 novel coronavirus (2019-nCoV), has spread rapidly across the globe, creating an unparalleled global health burden and spurring a deepening economic crisis. As of July 7th, 2020, almost seven months into the outbreak, there are no approved vaccines and few treatments available. Developing drugs that target multiple points in the viral life cycle could serve as a strategy to tackle the current as well as future coronavirus pandemics. Here we leverage the power of our recently developed in silico screening platform, VirtualFlow, to identify inhibitors that target SARS-CoV-2. VirtualFlow is able to efficiently harness the power of computing clusters and cloud-based computing platforms to carry out ultra-large scale virtual screens. In this unprecedented structure-based multi-target virtual screening campaign, we have used VirtualFlow to screen an average of approximately 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets in the cloud. In addition to targeting the active sites of viral enzymes, we also target critical auxiliary sites such as functionally important protein-protein interaction interfaces. This multi-target approach not only increases the likelihood of finding a potent inhibitor, but could also help identify a collection of anti-coronavirus drugs that would retain efficacy in the face of viral mutation. Drugs belonging to different regimen classes could be combined to develop possible combination therapies, and top hits that bind at highly conserved sites would be potential candidates for further development as coronavirus drugs. Here, we present the top 200 in silico hits for each target site. While in-house experimental validation of some of these compounds is currently underway, we want to make this array of potential inhibitor candidates available to researchers worldwide in consideration of the pressing need for fast-tracked drug development.}, language = {en} } @article{GorgullaBoeszoermnyiWangetal., author = {Gorgulla, Christoph and Boeszoermnyi, Andras and Wang, Zi-Fu and Fischer, Patrick D. and Coote, Paul and Das, Krishna M. Padmanabha and Malets, Yehor S. and Radchenko, Dmytro S. and Moroz, Yurii and Scott, David A. and Fackeldey, Konstantin and Hoffmann, Moritz and Iavniuk, Iryna and Wagner, Gerhard and Arthanari, Haribabu}, title = {An open-source drug discovery platform enables ultra-large virtual screens}, series = {Nature}, volume = {580}, journal = {Nature}, publisher = {Springer Nature}, doi = {https://doi.org/10.1038/s41586-020-2117-z}, pages = {663 -- 668}, abstract = {On average, an approved drug today costs \$2-3 billion and takes over ten years to develop1. In part, this is due to expensive and time-consuming wet-lab experiments, poor initial hit compounds, and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening (SBVS) has the potential to mitigate these problems. With SBVS, the quality of the hits improves with the number of compounds screened2. However, despite the fact that large compound databases exist, the ability to carry out large-scale SBVSs on computer clusters in an accessible, efficient, and flexible manner has remained elusive. Here we designed VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large ligand libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we have prepared the largest and freely available ready-to-dock ligand library available, with over 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened over 1 billion compounds and discovered a small molecule inhibitor (iKeap1) that engages KEAP1 with nanomolar affinity (Kd = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. We also identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify binders with high affinity for target proteins.}, language = {en} } @article{GorgullaCınaroğluFischeretal., author = {Gorgulla, Christoph and {\c{C}}{\i}naroğlu, S{\"u}leyman and Fischer, Patrick D. and Fackeldey, Konstantin and Wagner, Gerhard and Arthanari, Haribabu}, title = {VirtualFlow Ants—Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization}, series = {Special Issue Artificial Intelligence \& Deep Learning Approaches for Structural Bioinformatics}, volume = {22}, journal = {Special Issue Artificial Intelligence \& Deep Learning Approaches for Structural Bioinformatics}, number = {11}, doi = {https://doi.org/10.3390/ijms22115807}, pages = {5807}, abstract = {The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and the inclusion of experimental constraints. Here, we add support of PLANTS to VirtualFlow (VirtualFlow Ants), which adds a valuable method for primary virtual screenings and rescoring procedures. Furthermore, we have added support of ligand libraries in the MOL2 format, as well as on the fly conversion of ligand libraries which are in the PDBQT format to the MOL2 format to endow VirtualFlow Ants with an increased flexibility regarding the ligand libraries. The on the fly conversion is carried out with Open Babel and the program SPORES. We applied VirtualFlow Ants to a test system involving KEAP1 on the Google Cloud up to 128,000 CPUs, and the observed scaling behavior is approximately linear. Furthermore, we have adjusted several central docking parameters of PLANTS (such as the speed parameter or the number of ants) and screened 10 million compounds for each of the 10 resulting docking scenarios. We analyzed their docking scores and average docking times, which are key factors in virtual screenings. The possibility of carrying out ultra-large virtual screening with PLANTS via VirtualFlow Ants opens new avenues in computational drug discovery.}, language = {en} } @article{GorgullaFackeldeyWagneretal., author = {Gorgulla, Christoph and Fackeldey, Konstantin and Wagner, Gerhard and Arthanari, Haribabu}, title = {Accounting of Receptor Flexibility in Ultra-Large Virtual Screens with VirtualFlow Using a Grey Wolf Optimization Method}, series = {Supercomputing Frontiers and Innovations}, volume = {7}, journal = {Supercomputing Frontiers and Innovations}, number = {3}, doi = {10.14529/jsfi200301}, pages = {4 -- 12}, abstract = {Structure-based virtual screening approaches have the ability to dramatically reduce the time and costs associated to the discovery of new drug candidates. Studies have shown that the true hit rate of virtual screenings improves with the scale of the screened ligand libraries. Therefore, we have recently developed an open source drug discovery platform (VirtualFlow), which is able to routinely carry out ultra-large virtual screenings. One of the primary challenges of molecular docking is the circumstance when the protein is highly dynamic or when the structure of the protein cannot be captured by a static pose. To accommodate protein dynamics, we report the extension of VirtualFlow to allow the docking of ligands using a grey wolf optimization algorithm using the docking program GWOVina, which substantially improves the quality and efficiency of flexible receptor docking compared to AutoDock Vina. We demonstrate the linear scaling behavior of VirtualFlow utilizing GWOVina up to 128 000 CPUs. The newly supported docking method will be valuable for drug discovery projects in which protein dynamics and flexibility play a significant role.}, language = {en} } @article{GorgullaJayarajFackeldeyetal., author = {Gorgulla, Christoph and Jayaraj, Abhilash and Fackeldey, Konstantin and Arthanari, Haribabu}, title = {Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches}, series = {Current Opinion in Chemical Biology}, volume = {69}, journal = {Current Opinion in Chemical Biology}, doi = {10.1016/j.cbpa.2022.102156}, pages = {102156 -- 102156-12}, abstract = {Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein-protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.}, language = {en} } @article{FackeldeyGorgullaWeber, author = {Fackeldey, Konstantin and Gorgulla, Christoph and Weber, Marcus}, title = {Neue Medikamente dank Supercomputern}, series = {Spektrum der Wissenschaft}, journal = {Spektrum der Wissenschaft}, number = {11}, pages = {40 -- 46}, abstract = {Die aktuelle Pandemie verdeutlicht, wie wichtig es ist, rasch geeignete Arzneimittel zu finden. In Computer­simulationen gelingt das erheblich schneller als im Labor. Gegen das Coronavirus ließen sich auf diese Weise bereits Wirkstoffkandidaten identifizieren.}, language = {de} } @article{GorgullaNigamKoopetal., author = {Gorgulla, Christoph and Nigam, AkshatKumar and Koop, Matt and Selim {\c{C}}{\i}naroğlu, S{\"u}leyman and Secker, Christopher and Haddadnia, Mohammad and Kumar, Abhishek and Malets, Yehor and Hasson, Alexander and Li, Minkai and Tang, Ming and Levin-Konigsberg, Roni and Radchenko, Dmitry and Kumar, Aditya and Gehev, Minko and Aquilanti, Pierre-Yves and Gabb, Henry and Alhossary, Amr and Wagner, Gerhard and Aspuru-Guzik, Al{\´a}n and Moroz, Yurii S. and Fackeldey, Konstantin and Arthanari, Haribabu}, title = {VirtualFlow 2.0 - The Next Generation Drug Discovery Platform Enabling Adaptive Screens of 69 Billion Molecules}, series = {bioRxiv}, journal = {bioRxiv}, doi = {10.1101/2023.04.25.537981}, language = {en} } @article{SchimunekSeidlElezetal.2023, author = {Schimunek, Johannes and Seidl, Philipp and Elez, Katarina and Hempel, Tim and Le, Tuan and No{\´e}, Frank and Olsson, Simon and Raich, Llu{\´i}s and Winter, Robin and Gokcan, Hatice and Gusev, Filipp and Gutkin, Evgeny M. and Isayev, Olexandr and Kurnikova, Maria G. and Narangoda, Chamali H. and Zubatyuk, Roman and Bosko, Ivan P. and Furs, Konstantin V. and Karpenko, Anna D. and Kornoushenko, Yury V. and Shuldau, Mikita and Yushkevich, Artsemi and Benabderrahmane, Mohammed B. and Bousquet-Melou, Patrick and Bureau, Ronan and Charton, Beatrice and Cirou, Bertrand C. and Gil, G{\´e}rard and Allen, William J. and Sirimulla, Suman and Watowich, Stanley and Antonopoulos, Nick and Epitropakis, Nikolaos and Krasoulis, Agamemnon and Itsikalis, Vassilis and Theodorakis, Stavros and Kozlovskii, Igor and Maliutin, Anton and Medvedev, Alexander and Popov, Petr and Zaretckii, Mark and Eghbal-Zadeh, Hamid and Halmich, Christina and Hochreiter, Sepp and Mayr, Andreas and Ruch, Peter and Widrich, Michael and Berenger, Francois and Kumar, Ashutosh and Yamanishi, Yoshihiro and Zhang, Kam Y. J. and Bengio, Emmanuel and Bengio, Yoshua and Jain, Moksh J. and Korablyov, Maksym and Liu, Cheng-Hao and Marcou, Gilles and Glaab, Enrico and Barnsley, Kelly and Iyengar, Suhasini M. and Ondrechen, Mary Jo and Haupt, V. Joachim and Kaiser, Florian and Schroeder, Michael and Pugliese, Luisa and Albani, Simone and Athanasiou, Christina and Beccari, Andrea and Carloni, Paolo and D'Arrigo, Giulia and Gianquinto, Eleonora and Goßen, Jonas and Hanke, Anton and Joseph, Benjamin P. and Kokh, Daria B. and Kovachka, Sandra and Manelfi, Candida and Mukherjee, Goutam and Mu{\~n}iz-Chicharro, Abraham and Musiani, Francesco and Nunes-Alves, Ariane and Paiardi, Giulia and Rossetti, Giulia and Sadiq, S. Kashif and Spyrakis, Francesca and Talarico, Carmine and Tsengenes, Alexandros and Wade, Rebecca C. and Copeland, Conner and Gaiser, Jeremiah and Olson, Daniel R. and Roy, Amitava and Venkatraman, Vishwesh and Wheeler, Travis J. and Arthanari, Haribabu and Blaschitz, Klara and Cespugli, Marco and Durmaz, Vedat and Fackeldey, Konstantin and Fischer, Patrick D. and Gorgulla, Christoph and Gruber, Christian and Gruber, Karl and Hetmann, Michael and Kinney, Jamie E. and Padmanabha Das, Krishna M. and Pandita, Shreya and Singh, Amit and Steinkellner, Georg and Tesseyre, Guilhem and Wagner, Gerhard and Wang, Zi-Fu and Yust, Ryan J. and Druzhilovskiy, Dmitry S. and Filimonov, Dmitry A. and Pogodin, Pavel V. and Poroikov, Vladimir and Rudik, Anastassia V. and Stolbov, Leonid A. and Veselovsky, Alexander V. and De Rosa, Maria and De Simone, Giada and Gulotta, Maria R. and Lombino, Jessica and Mekni, Nedra and Perricone, Ugo and Casini, Arturo and Embree, Amanda and Gordon, D. Benjamin and Lei, David and Pratt, Katelin and Voigt, Christopher A. and Chen, Kuang-Yu and Jacob, Yves and Krischuns, Tim and Lafaye, Pierre and Zettor, Agn{\`e}s and Rodr{\´i}guez, M. Luis and White, Kris M. and Fearon, Daren and Von Delft, Frank and Walsh, Martin A. and Horvath, Dragos and Brooks III, Charles L. and Falsafi, Babak and Ford, Bryan and Garc{\´i}a-Sastre, Adolfo and Yup Lee, Sang and Naffakh, Nadia and Varnek, Alexandre and Klambauer, G{\"u}nter and Hermans, Thomas M.}, title = {A community effort in SARS-CoV-2 drug discovery}, series = {Molecular Informatics}, volume = {43}, journal = {Molecular Informatics}, number = {1}, doi = {https://doi.org/10.1002/minf.202300262}, pages = {e202300262}, year = {2023}, language = {en} } @article{SeckerFackeldeyWeberetal., author = {Secker, Christopher and Fackeldey, Konstantin and Weber, Marcus and Ray, Sourav and Gorgulla, Christoph and Sch{\"u}tte, Christof}, title = {Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists}, series = {Journal of Cheminformatics}, volume = {15}, journal = {Journal of Cheminformatics}, doi = {10.1186/s13321-023-00746-4}, abstract = {Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the μ-opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding affinity to the MOR at acidic pH while minimizing it at neutral pH, thus combining two conflicting objectives. Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported β-fluorofentanyls and -morphines show an increased specificity for the MOR at acidic pH when compared to fentanyl and morphine. We further applied our approach to screen a >50K ligand library identifying novel molecules with pH-specific predicted binding affinities to the MOR. The presented differential docking pipeline can be applied to perform multi-objective affinity optimization to identify safer and more specific drug candidates at large scale.}, language = {en} }