Package: conplex-dti
ConPLex🚧🚧 Please note that ConPLex is currently a pre-release and is actively being developed. For the code used to generate our PNAS results, see themanuscript code🚧🚧HomepageDocumentationAbstractSequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs. However, current computational techniques fail to simultaneously meet these goals, often sacrificing performance on one to achieve the others. We develop a deep learning model, ConPLex, successfully leveraging the advances in pre-trained protein language models ("PLex") and employing  a novel  protein-anchored contrastive co-embedding ("Con") to outperform state-of-the-art approaches. ConPLex achieves high accuracy, broad adaptivity to unseen data, and specificity against decoy compounds. It makes predictions of binding based on the distance between learned representations, enabling predictions at the scale of massive compound libraries and the human proteome. Experimental testing of 19 kinase-drug interaction predictions validated 12 interactions, including four with sub-nanomolar affinity, plus a novel strongly-binding EPHB1 inhibitor ($K_D = 1.3nM$). Furthermore, ConPLex embeddings are interpretable, which enables us to visualize the drug-target embedding space and use embeddings to characterize the function of human cell-surface proteins. We anticipate ConPLex will facilitate novel drug discovery by making highly sensitive in-silico drug screening feasible at genome scale.InstallationInstall from PyPIYou should first have a version ofcudatoolkitcompatible with your system installed. Then runpipinstallconplex-dti
conplex-dti--helpCompile from Sourcegitclonehttps://github.com/samsledje/ConPLex.gitcdConPLex
condacreate-nconplex-dtipython=3.9
condaactivateconplex-dti
makepoetry-downloadexportPATH="[poetry  install  location]:$PATH"exportPYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring
makeinstall
conplex-dti--helpUsageDownload benchmark data sets and pre-trained modelsconplex-dtidownload--todatasets--benchmarksdavisbindingdbbiosnapbiosnap_protbiosnap_moldudeconplex-dtidownload--to.--modelsConPLex_v1_BindingDBRun benchmark trainingconplex-dtitrain--run-idTestRun--configconfig/default_config.yamlMake predictions with a trained modelconplex-dtipredict--data-file[pairpredictfile].tsv--model-path./models/ConPLex_v1_BindingDB.pt--outfile./results.tsvFormat of[pair predict file].tsvshould be[protein ID]\t[molecule ID]\t[protein Sequence]\t[molecule SMILES]Visualize co-embedding space...ReferenceIf you use ConPLex, please citeContrastive learning in protein language space predicts interactions between drugs and protein targetsby Rohit Singh*, Samuel Sledzieski*, Bryan Bryson, Lenore Cowen and Bonnie Berger.@article{singh2023contrastive,title={Contrastivelearninginproteinlanguagespacepredictsinteractionsbetweendrugsandproteintargets},author={Singh,RohitandSledzieski,SamuelandBryson,BryanandCowen,LenoreandBerger,Bonnie},journal={ProceedingsoftheNationalAcademyofSciences},volume={120},number={24},pages={e2220778120},year={2023},publisher={NationalAcadSciences}}Thanks to Ava Amini, Kevin Yang, and Sevahn Vorperian from MSR New England for suggesting the use of the triplet distance contrastive loss function without the sigmoid activation. The default has now been changed. For the original formulation with the sigmoid activation, you can set the--use-sigmoid-cosineflag during training.Manuscript CodeCode used to generate results in the manuscript can be found in thedevelopment repository