EvE Bio and DrugBank Partner to Integrate Record-Scale Pharmome Mapping Data into Drug Discovery Platform
核心洞察
EvE Bio (搜索)'s comprehensive pharmome (搜索) dataset containing 385,572 rigorously-tested drug-target interactions (搜索) across 159 validated targets is being integrated into DrugBank (搜索)'s intelligence platform.
The dataset surpasses previous public state-of-the-art by testing 1,397 small molecule compounds (搜索), compared to Novartis's 2023 study of 800 drugs against 105 targets covering only 2% of the druggable genome.
EvE Bio (搜索)'s data is already powering FutureHouse (搜索)'s Ether0 chemistry AI model and will be available through the Hugging Face platform for machine learning experts.
EvE Bio (搜索), a focused research organization dedicated to systematically mapping the human pharmome (搜索), has announced a strategic partnership with DrugBank (搜索) to integrate its record-scale drug-target interaction dataset into DrugBank's intelligence platform. The collaboration brings together EvE Bio's comprehensive pharmome mapping data with DrugBank's established drug discovery and development platform.
Unprecedented Scale of Drug-Target Interaction Data
EvE Bio (搜索)'s seventh data release represents a significant milestone in pharmome (搜索) mapping, encompassing 385,572 rigorously-tested drug-target interactions (搜索). The dataset includes 1,397 small molecule compounds (搜索) tested for both agonism and antagonism against 159 druggable targets, establishing a new benchmark for comprehensive drug-target profiling.
This achievement surpasses the previous public state-of-the-art documented in a 2023 report from Novartis scientists, who profiled approximately 800 drugs against 105 protein targets, covering roughly 2% of the 'druggable genome'. EvE Bio (搜索)'s systematic approach addresses the critical knowledge gap in understanding how medicines interact beyond their primary intended targets.
"Most medicines interact with more than their primary, intended target," the organizations noted. "These unintended (and frequently unknown) 'off-targets (搜索)' can cause side effects, but can also suggest new therapeutic uses."
AI Integration and Platform Accessibility
The pharmome (搜索) data has already demonstrated its value in artificial intelligence applications. FutureHouse (搜索)'s Ether0, a 24-billion-parameter scientific reasoning model for chemistry, utilized EvE Bio (搜索)'s data as a ground truth dataset for training and evaluation. The model was trained via reinforcement learning on 640,730 experimentally grounded problems across 375 tasks.
According to FutureHouse (搜索)'s preprint, the resulting model "exceeds general-purpose chemistry models, frontier models, and human experts on molecular design tasks" and is "also more data efficient relative to specialized models." EvE Bio (搜索)'s data will now be available programmatically and interactively through the Hugging Face platform, providing streamlined access for machine learning and AI experts.
Strategic Applications in Drug Discovery
The integration into DrugBank (搜索)'s platform enables multiple strategic applications across the drug discovery landscape. Early integration work has identified clear use cases including exploring off-target liabilities, surfacing repurposing opportunities, enriching drug-biology-disease relationships, and enhancing cheminformatics insights.
"EvE Bio (搜索)'s data reveals a new layer of information about how drugs work," said Michael Wilson, Chief Product Officer of DrugBank (搜索). "By bringing this pharmome (搜索) map into our platform, we're giving teams unprecedented insights on critical questions relating to off-target interactions, repurposing opportunities, and deep interrogations of outcomes in trials."
Leadership Transition and Organizational Growth
Concurrent with the partnership announcement, EvE Bio (搜索) has undergone a leadership transition. Elaine McVey Houskeeper, founding Director of Data Science, has been appointed Chief Executive Officer, while founding CEO William Busa will continue as Chief Scientific Officer.
"EvE has built a world-class engine to create trustworthy, assay-grounded pharmacological interaction data and integrate it directly into the tools scientists already use," said Houskeeper. "As we move into the next phase of EvE's growth as a Focused Research Organization, I'm so excited by how far we've come - and by the potential to expand our screening approach into new libraries and compounds."
Systematic Approach to Pharmome Mapping
EvE Bio (搜索)'s high-throughput process has been specifically designed to produce robust, verifiable datapoints for every potential drug-target interaction, whether active or inactive. This systematic approach ensures comprehensive coverage and reliability of the resulting dataset.
"We founded EvE to bring a new field - pharmome (搜索) mapping - to the aid of the drug discovery and development process," said William Busa, co-founder and CSO of EvE Bio (搜索). "We're quickly onboarding new, fully validated assays and releasing the data multiple times a year at data.evebio.org."
The organization's data drops are made available as a public good, reflecting its mission to accelerate drug discovery and development across the scientific community. The partnership with DrugBank (搜索) extends this accessibility by integrating the data into familiar tools used by chemists, AI model developers, drug developers, and clinicians.
Broader Impact on Scientific Research
Anastasia Gamick, co-founder and President of Convergent Research (搜索), which supports EvE Bio (搜索) as a Focused Research Organization, emphasized the transformative potential of pharmome (搜索) mapping. "Just as the effort to map the human genome kicked off a wave of research breakthroughs and economic developments, we believe mapping how compounds interact with human biology can yield meaningful and long-needed benefits for millions waiting for cures and innovations around the world."
The partnership represents a significant step toward comprehensive understanding of drug-target interactions (搜索), potentially accelerating the identification of new therapeutic applications for existing drugs while improving safety profiles through better prediction of off-target effects.