TY - JOUR A1 - Gorgulla, Christoph A1 - Boeszoermnyi, Andras A1 - Wang, Zi-Fu A1 - Fischer, Patrick D. A1 - Coote, Paul A1 - Das, Krishna M. Padmanabha A1 - Malets, Yehor S. A1 - Radchenko, Dmytro S. A1 - Moroz, Yurii A1 - Scott, David A. A1 - Fackeldey, Konstantin A1 - Hoffmann, Moritz A1 - Iavniuk, Iryna A1 - Wagner, Gerhard A1 - Arthanari, Haribabu T1 - An open-source drug discovery platform enables ultra-large virtual screens JF - Nature N2 - 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. Y1 - 2020 U6 - https://doi.org/https://doi.org/10.1038/s41586-020-2117-z VL - 580 SP - 663 EP - 668 PB - Springer Nature ER - TY - JOUR A1 - Petkova, Mariela D. A1 - Januszewski, Michał A1 - Blakely, Tim A1 - Herrera, Kristian J. A1 - Schuhknecht, Gregor F.P. A1 - Tiller, Robert A1 - Choi, Jinhan A1 - Schalek, Richard L. A1 - Boulanger-Weill, Jonathan A1 - Peleg, Adi A1 - Wu, Yuelong A1 - Wang, Shuohong A1 - Troidl, Jakob A1 - Vohra, Sumit Kumar A1 - Wei, Donglai A1 - Lin, Zudi A1 - Bahl, Armin A1 - Tapia, Juan Carlos A1 - Iyer, Nirmala A1 - Miller, Zachary T. A1 - Hebert, Kathryn B. A1 - Pavarino, Elisa C. A1 - Taylor, Milo A1 - Deng, Zixuan A1 - Stingl, Moritz A1 - Hockling, Dana A1 - Hebling, Alina A1 - Wang, Ruohong C. A1 - Zhang, Lauren L. A1 - Dvorak, Sam A1 - Faik, Zainab A1 - King, Jr., Kareem I. A1 - Goel, Pallavi A1 - Wagner-Carena, Julian A1 - Aley, David A1 - Chalyshkan, Selimzhan A1 - Contreas, Dominick A1 - Li, Xiong A1 - Muthukumar, Akila V. A1 - Vernaglia, Marina S. A1 - Carrasco, Teodoro Tapia A1 - Melnychuck, Sofia A1 - Yan, TingTing A1 - Dalal, Ananya A1 - DiMartino, James A1 - Brown, Sam A1 - Safo-Mensa, Nana A1 - Greenberg, Ethan A1 - Cook, Michael A1 - Finley, Samantha A1 - Flynn, Miriam A. A1 - Hopkins, Gary Patrick A1 - Kovalyak, Julie A1 - Leonard, Meghan A1 - Lohff, Alanna A1 - Ordish, Christopher A1 - Scott, Ashley L. A1 - Takemura, Satoko A1 - Smith, Claire A1 - Walsh, John J. A1 - Berger, Daniel R. A1 - Pfister, Hanspeter A1 - Berg, Stuart A1 - Knecht, Christopher A1 - Meissner, Geoffrey W. A1 - Korff, Wyatt A1 - Ahrens, Misha B A1 - Jain, Viren A1 - Lichtman, Jeff W. A1 - Engert, Florian T1 - A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain JF - bioRxiv N2 - We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory (vglut2a) and inhibitory (gad1b) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing >180,000 segmented soma, >40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the dataset’s utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain. Y1 - 2025 U6 - https://doi.org/10.1101/2025.06.10.658982 ER -