Computational deep dive surfaces unexplored world of cancer drug targets

One person’s side effect could be another person’s treatment if we expand our perspective on small molecule drug targets, according to a new study published November 5, 2025, in npj Precision Oncology.

“The kinds of small molecules representing many of our medicines are rarely found in nature, so they haven’t evolved to carry out a specific task,” said Sanju Sinha, PhD, an assistant professor in the Cancer Metabolism and Microenvironment Program at Sanford Burnham Prebys Medical Discovery Institute. “Sometimes the field looks at these drugs with tunnel vision in terms of them having a single target along with some side effects labeled as ‘off-target effects.’

“Taking a more holistic view reveals that small molecules can have different targets and effects depending on the disease and cell type, and we can use this knowledge to repurpose more drugs to treat more patients.”

Starting during his time training at the National Cancer Institute, Sinha has investigated the malleability of small molecule drugs by developing a computational tool called DeepTarget.  Rather than relying on the drugs’ chemical structures, Sinha and his collaborators used data from large-scale genetic and drug screening experiments in cancer cells. Their dataset included comprehensive data for 1450 drugs across 371 cancer cell lines from the Dependency Map (DepMap) Consortium’s efforts to create an atlas of cancer vulnerabilities.

In seven out of eight tests comparing computational predictions of primary cancer drug targets to existing data on drug-target pairs, DeepTarget performed better than current state-of-the-art tools including RoseTTAFold All-Atom and Chai-1. The research team also demonstrated that DeepTarget can predict if drugs have preferential effects on typical, non-mutated target proteins or their mutant forms, as well as determine drugs’ secondary targets.

The scientists benchmarked DeepTarget’s capability to predict secondary targets by comparing its performance to existing data on 64 cancer drugs known to have more than one target.

“Being able to predict these secondary targets is important because many FDA-approved drugs and new drugs in clinical development have them,” said Sinha, lead author of the manuscript. “If we can see them more as features rather than bugs, we can take advantage of these targets to improve drug repurposing.”

To validate their findings, the research team conducted two experimental case studies, including one on Ibrutinib, an FDA-approved drug for blood cancer. Prior clinical research showed that Ibrutinib could treat lung cancer even though the drug’s primary target—a protein called Bruton’s tyrosine kinase (BTK)—is not present in lung tumors.

In collaboration with the lab of co-corresponding author Ani Deshpande, PhD, a professor in the Cancer Genome and Epigenetics Program at Sanford Burnham Prebys, the scientists tested DeepTarget’s prediction that Ibrutinib was killing lung cancer cells by acting on a secondary target protein called epidermal growth factor receptor (EGFR).

“In consulting DeepTarget, if we only focused on blood tumors, then BTK was the primary target,” said Sinha. “If we changed our focus to solid tumors, then a mutant, oncogenic form of EGFR became the primary target, so this was a clear example of a context-specific target.”

The researchers compared the effects of Ibrutinib on cancer cells with and without the cancerous mutant EGFR. The cells harboring the mutant form were more sensitive to the drug, validating EGFR as a target of Ibrutinib.

“We believe that the tool’s superior performance in real-world scenarios is due to it more closely mirroring real-world drug mechanisms, where cellular context and pathway-level effects often play crucial roles beyond direct binding interactions,” said Sinha.

“It also underscores DeepTarget’s potential to accelerate drug development and repurposing efforts as a complementary approach alongside structural methods focused on chemical binding.”

Moving forward, Sinha wants to build on what the team has learned to create new small molecule candidate drugs.

“The potential pool of chemicals is much larger than what we are able to screen for even with modern, high-throughput drug screening methods,” said Sinha.

“Improving treatment options for cancer and for related and even more complex conditions like aging will depend on us improving both our ways to understand the biology, as well as ways to modulate it with therapies.”

Eytan Ruppin, MD, PhD, the deputy director of the Translational Research Institute at Cedars-Sinai and former founding chief of the Cancer Data Science Lab at the National Cancer Institute, is the senior and co-corresponding author of the study.

Additional authors include:

• Neelam Sinha, Marlenne Perales, Lihe Liu, Kyle Alvarez, Kevin Tharp, Jianhua Zhao and Ranjit Kumar from Sanford Burnham Prebys
• Thomas Cantore, Sumeet Patiyal, Sumit Mukherjee, Sanna Madan, Trinh Nguyen, Greg Flanigan, Daoud Meerzaman, John A. Beutler and Barry R. O’Keefe from the National Cancer Institute
• Adi Tarrab and Uri Ben-David from Tel Aviv University

The study was supported by the National Institutes of Health, National Cancer Institute and Sanford Burnham Prebys.

The study’s DOI is 10.1038/s41698-025-01111-4.