A Shortcut for Drug Discovery

For most human proteins, there are no small molecules known to bind them chemically (so called "ligands"). Ligands frequently represent important starting points for drug development but this knowledge gap critically hampers the development of novel medicines. Researchers at CeMM, in a collaboration with Pfizer, have now leveraged and scaled a method to measure the binding activity of hundreds of small molecules against thousands of human proteins. This large-scale study revealed tens of thousands of ligand-protein interactions that can now be explored for the development of chemical tools and therapeutics. Moreover, powered by machine learning and artificial intelligence, it allows unbiased predictions of how small molecules interact with all proteins present in living human cells. These groundbreaking results have been published in the journal Science (DOI: 10.1126/science.adk5864), and all generated data and models are freely available for the scientific community.

The majority of all drugs are small molecules that influence the activity of proteins. These small molecules - if well understood - are also invaluable tools to characterize the behavior of proteins and to do basic biological research. Given these essential roles, it is surprising that for more than 80 percent of all proteins, no small-molecule binders have been identified so far. This hinders the development of novel drugs and therapeutic strategies, but likewise prevents novel biological insights into health and disease.

To close this gap, researchers at CeMM in collaboration with Pfizer have expanded and scaled an experimental platform that enables them to measure how hundreds of small molecules with various chemical structures interact with all expressed proteins in living cells. This yielded a rich catalog of tens of thousands of ligand-protein interactions than can now be further optimized to represent starting points for further therapeutic development. In their study, the team led by CeMM PI Georg Winter has exemplified this by developing small-molecule binders of cellular transporters, components of the cellular degradation machinery and to understudied proteins involved in cellular signal transduction. Moreover, taking advantage of the large dataset, machine learning and artificial intelligence models were developed that can predict how additional small molecules interact with proteins expressed in living human cells.

"We were amazed to see how artificial intelligence and machine learning can elevate our understanding of small-molecule behavior in human cells. We hope that our catalog of small molecule-protein interactions and the associated artificial intelligence models can now provide a shortcut in drug discovery approaches," says Georg Winter. To maximize the potential impact and usefulness for the scientific community, all data and models are made freely available through a web application. "This was an outstanding partnership between industry and academia. We are delighted to present the results which were obtained through three years of close collaboration and teamwork between the groups. It’s been a great project," says Dr Patrick Verhoest, Vice President and Head of Medicine Design at Pfizer.

Offensperger F, Tin G, Duran-Frigola M, Hahn E, Dobner S, Ende CWA, Strohbach JW, Rukavina A, Brennsteiner V, Ogilvie K, Marella N, Kladnik K, Ciuffa R, Majmudar JD, Field SD, Bensimon A, Ferrari L, Ferrada E, Ng A, Zhang Z, Degliesposti G, Boeszoermenyi A, Martens S, Stanton R, Müller AC, Hannich JT, Hepworth D, Superti-Furga G, Kubicek S, Schenone M, Winter GE.
Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells.
Science. 2024 Apr 26;384(6694):eadk5864. doi: 10.1126/science.adk5864

Most Popular Now

Stepping Hill Hospital Announced as SPAR…

Stepping Hill Hospital, part of Stockport NHS Foundation Trust, has replaced its bedside units with state-of-the art devices running a full range of information, engagement, communications and productivity apps, to...

DMEA 2025: Digital Health Worldwide in B…

8 - 10 April 2025, Berlin, Germany. From the AI Act, to the potential of the European Health Data Space, to the power of patient data in Scandinavia - DMEA 2025...

Is AI in Medicine Playing Fair?

As artificial intelligence (AI) rapidly integrates into health care, a new study by researchers at the Icahn School of Medicine at Mount Sinai reveals that all generative AI models may...

New System for the Early Detection of Au…

A team from the Human-Tech Institute-Universitat Politècnica de València has developed a new system for the early detection of Autism Spectrum Disorder (ASD) using virtual reality and artificial intelligence. The...

Generative AI's Diagnostic Capabili…

The use of generative AI for diagnostics has attracted attention in the medical field and many research papers have been published on this topic. However, because the evaluation criteria were...

Diagnoses and Treatment Recommendations …

A new study led by Prof. Dan Zeltzer, a digital health expert from the Berglas School of Economics at Tel Aviv University, compared the quality of diagnostic and treatment recommendations...

AI Tool can Track Effectiveness of Multi…

A new artificial intelligence (AI) tool that can help interpret and assess how well treatments are working for patients with multiple sclerosis (MS) has been developed by UCL researchers. AI uses...

Surrey and Sussex Healthcare NHS Trust g…

Surrey and Sussex Healthcare NHS Trust has marked an important milestone in connecting busy radiologists across large parts of South East England, following the successful go live of Sectra's enterprise...

Dr Jason Broch Joins the Highland Market…

The Highland Marketing advisory board has welcomed a new member - Dr Jason Broch, a GP and director with a strong track record in the NHS and IT-enabled transformation. Dr Broch...

DMEA 2025 Ends with Record Attendance an…

8 - 10 April 2025, Berlin, Germany. DMEA 2025 came to a successful close with record attendance and an impressive program. 20,500 participants attended Europe's leading digital health event over the...

Multi-Resistance in Bacteria Predicted b…

An AI model trained on large amounts of genetic data can predict whether bacteria will become antibiotic-resistant. The new study shows that antibiotic resistance is more easily transmitted between genetically...

AI-Driven Smart Devices to Transform Hea…

AI-powered, internet-connected medical devices have the potential to revolutionise healthcare by enabling early disease detection, real-time patient monitoring, and personalised treatments, a new study suggests. They are already saving lives...