Powerful generative models of protein-ligand structure have recently been proposed, but few of these methods support both flexible protein-ligand docking and affinity estimation. Of those that do, none can directly model multiple binding ligands concurrently or have been rigorously benchmarked on pharmacologically relevant drug targets, hindering their widespread adoption in drug discovery efforts. In this work, we propose FlowDock, a deep geometric generative model based on conditional flow matching that learns to directly map unbound (apo) structures to their bound (holo) counterparts for an arbitrary number of binding ligands. Furthermore, FlowDock provides predicted structural confidence scores and binding affinity values with each of its generated protein-ligand complex structures, enabling fast virtual screening of new (multi-ligand) drug targets. For the commonly-used PoseBusters Benchmark dataset, FlowDock achieves a 51% blind docking success rate using unbound (apo) protein input structures and without any information derived from multiple sequence alignments, and for the challenging new DockGen-E dataset, FlowDock matches the performance of single-sequence Chai-1 for binding pocket generalization. Additionally, in the ligand category of the 16th community-wide Critical Assessment of Techniques for Structure Prediction (CASP16), FlowDock ranked among the top-5 methods for pharmacological binding affinity estimation across 140 protein-ligand complexes, demonstrating the efficacy of its learned representations in virtual screening. Source code, data, and pre-trained models are available at https://github.com/BioinfoMachineLearning/FlowDock.

Since GitHub integrated ChatOps into their operational strata, the concept has garnered a good deal of excitement. Essentially, ChatOps moves system operations into a group chat room, enabling developers to collaborate, initiate tests, deploy software, and build a company culture all from a single unified command line. A novel idea which some teams have implemented successfully, ChatOps could increase efficiency, represents a move toward a more transparent work environment, and allows distributed software development teams to flourish. In this post we outline the possible benefits of a ChatOps approach and list over 12 pre-made ChatOps frameworks, bots, and tools that teams can utilize to develop their own conversational user interface into their software development workflow. Many teams already use a chat room like Slack or Hipchat to share information, collaborate, and build knowledge bases around particular projects.