@article{GorgullaBoeszoermnyiWangetal.2020, 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}, volume = {580}, journal = {Nature}, publisher = {Springer Nature}, doi = {https://doi.org/10.1038/s41586-020-2117-z}, pages = {663 -- 668}, year = {2020}, 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{PetkovaJanuszewskiBlakelyetal.2025, author = {Petkova, Mariela D. and Januszewski, MichaƂ and Blakely, Tim and Herrera, Kristian J. and Schuhknecht, Gregor F.P. and Tiller, Robert and Choi, Jinhan and Schalek, Richard L. and Boulanger-Weill, Jonathan and Peleg, Adi and Wu, Yuelong and Wang, Shuohong and Troidl, Jakob and Vohra, Sumit Kumar and Wei, Donglai and Lin, Zudi and Bahl, Armin and Tapia, Juan Carlos and Iyer, Nirmala and Miller, Zachary T. and Hebert, Kathryn B. and Pavarino, Elisa C. and Taylor, Milo and Deng, Zixuan and Stingl, Moritz and Hockling, Dana and Hebling, Alina and Wang, Ruohong C. and Zhang, Lauren L. and Dvorak, Sam and Faik, Zainab and King, Jr., Kareem I. and Goel, Pallavi and Wagner-Carena, Julian and Aley, David and Chalyshkan, Selimzhan and Contreas, Dominick and Li, Xiong and Muthukumar, Akila V. and Vernaglia, Marina S. and Carrasco, Teodoro Tapia and Melnychuck, Sofia and Yan, TingTing and Dalal, Ananya and DiMartino, James and Brown, Sam and Safo-Mensa, Nana and Greenberg, Ethan and Cook, Michael and Finley, Samantha and Flynn, Miriam A. and Hopkins, Gary Patrick and Kovalyak, Julie and Leonard, Meghan and Lohff, Alanna and Ordish, Christopher and Scott, Ashley L. and Takemura, Satoko and Smith, Claire and Walsh, John J. and Berger, Daniel R. and Pfister, Hanspeter and Berg, Stuart and Knecht, Christopher and Meissner, Geoffrey W. and Korff, Wyatt and Ahrens, Misha B and Jain, Viren and Lichtman, Jeff W. and Engert, Florian}, title = {A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain}, journal = {bioRxiv}, doi = {10.1101/2025.06.10.658982}, year = {2025}, abstract = {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.}, language = {en} }