Five AI-driven Drug Discovery Companies Enabling Precision Oncology
Artificial Intelligence (AI) is steadily making its presence felt across various sectors, including the pharmaceutical industry. Among the multiple applications of AI in this field, immunotherapy—a treatment method that utilizes the body's immune system to combat diseases, notably cancer—is seeing a significant influence. The integration of AI can potentially enhance treatments and patient care by making it more precise and personalized.
Making sense of complex biological systems
The human immune system is a densely interconnected network involving numerous immune cells, signaling molecules, and genes. The interactions within this network are intricate, dynamic, and largely unpredictable. The vast amount of biological data often surpasses the comprehension ability of humans and the scope of traditional statistical methods.
This is where AI, with its capacity to understand and predict from data, comes into play. Deep learning algorithms can handle enormous amounts of genetic, proteomic, and clinical data, discern patterns and correlations, and contribute to the identification of new immunotherapeutic targets.
What is immunotherapy?
Immunotherapy refers to a set of medical treatments that use the body's natural defenses—the immune system—to identify, fight, and eliminate diseases, particularly cancer. Instead of directly attacking the disease, immunotherapies equip the immune system to recognize disease cells and stimulate an immune response against them. There are several types of immunotherapies, each with a different mechanism of action—checkpoint inhibitors, Chimeric Antigen Receptor T-cell (CAR-T cell) therapy, cancer vaccines, monoclonal antibodies, Immune system modulators.
Enhancing personalized immunotherapies
Personalized immunotherapies, such as CAR-T cell therapy, have shown promise in cancer treatment. In this therapy, T cells are removed from a patient's body, genetically modified to produce receptors (CARs) that target specific proteins on cancer cells, and then infused back into the patient.
However, predicting which patients will respond favorably to these therapies has been a persistent challenge. AI algorithms can process data from various sources, including genomics, clinical trials, and real-world evidence. This helps to discover biomarkers and predict patients' responses to treatment. Thus, AI can assist in selecting patients who are likely to benefit most from the therapy, leading to improved treatment outcomes and cost-effectiveness.
Refining Combination Therapies
Immunotherapies often yield better results when combined with other treatments. Yet, determining the optimal combination can be a time-consuming process. AI can aid in identifying effective drug combinations, potentially speeding up the process and the development of more efficient treatment strategies.
Improving Clinical Trials
Clinical trials play a pivotal role in the development of new immunotherapies. However, they can be expensive and lengthy. AI can potentially expedite the clinical trial process, right from patient selection to data collection and analysis. Predictive models powered by AI can identify suitable patients more accurately, reduce drop-out rates, and enable real-time data analysis.
Below is a list of 5 notable AI-driven drug discovery companies pushing the boundaries of what can be done to treat cancer:
Predictive Oncology
Predictive Oncology Inc. (NASDAQ: POAI) is a company focused on AI-driven drug discovery and development, specifically in the field of oncology. The company utilizes a proprietary artificial intelligence and machine learning platform that has been scientifically validated to predict with 92% accuracy if a tumor sample will respond to a specific drug compound.
The company's platform integrates genomics, digitized pathology data, and phenotype profiling to analyze heterogeneous responses across different drug treatments. These capabilities enable more precise drug-tumor pairings for subsequent in-vitro testing, and support the discovery of new biomarkers, targets, and potential drugs. In July 2024, the company expanded its AI and machine learning platform into biomarker discovery following successful results from a retrospective ovarian cancer study with UPMC Magee-Womens Hospital.
Predictive Oncology maintains a biobank with over 150,000 cryogenically preserved human tumor samples, covering 137 different tumor types. This biobank allows the company to account for patient heterogeneity in the early stages of drug development, which is critical for increasing the Probability of Technical Success (PTS) in clinical trials. The company published an August 2024 study confirming the long-term stability and viability of its 150,000 cryopreserved tumor samples.
In February 2025, the company reported positive results from its AI-driven drug repurposing program, which analyzed data on abandoned or discontinued compounds to identify new therapeutic opportunities for ovarian, colon, and breast cancers. By combining its tumor biobank with machine learning models, Predictive identified several drug candidates showing promising activity—some outperforming standard therapies—while reducing laboratory testing time by an estimated 18 months.
Following this update, Predictive Oncology and Switzerland-based Tecan Group reported successful use of cryopreserved patient tumor cells to generate 3D spheroid models for high-throughput drug screening. Presented at the SLAS 2025 Conference, the study showed that automated imaging and AI-based analysis improved the accuracy of drug response testing in breast, colon, and ovarian tumor samples from Predictive’s biobank.
In March 2025, Predictive Oncology reported developing predictive models for 21 novel natural compounds sourced from the University of Michigan’s Natural Products Discovery Core, using its active machine learning platform and live-cell tumor samples. The models identified several compounds with potent anti-tumor activity in breast, colon, and ovarian cancers, some outperforming the standard chemotherapy drug doxorubicin.
In June 2025, Predictive Oncology also developed exclusive 3D liver organoid models for Labcorp, designed to predict drug metabolism, transport, and hepatotoxicity in both human and rat systems.
In September 2025, Predictive Oncology partnered with the nonprofit Every Cure to accelerate drug repurposing for cancer treatment. The collaboration combines Predictive’s AI-driven tumor response platform and biobank with Every Cure’s AI models that match FDA-approved drugs to new disease indications, aiming to identify effective therapies for patients with limited treatment options more rapidly and at lower cost.
In October 2025, Predictive Oncology raised $343.5 million in private placements to create a treasury partly backed by digital assets instead of cash. The company received about $50.8M in cash and $292.7M in cryptocurrency used in cloud computing networks, saying the move will help secure access to computing resources for its AI-driven drug discovery work and diversify its financial holdings.
Predictive Oncology's platform facilitates critical go/no-go decisions in drug development, helping pharmaceutical companies optimize clinical trial design and accelerate development timelines. The company operates a CLIA-certified lab and GMP facilities to support its operations. Predictive Oncology is headquartered in Pittsburgh, PA, and is dedicated to improving the success rates of oncology drug development.
Lantern Pharma
Lantern Pharma Inc. (NASDAQ: LTRN), an innovative biopharmaceutical company, based in Dallas, USA, that is focused on transforming oncology drug discovery and development, stands out with its proprietary RADR artificial intelligence and machine learning platform. Using over 200 billion oncology-specific data points (up from 100 billion reported earlier in 2024) and a collection of advanced ML algorithms, Lantern aims to solve real-world, billion-dollar challenges in the oncology field.
In August 2025, Lantern Pharma publicly released an open-access AI platform for predicting blood–brain barrier permeability of small molecules with reported 94% accuracy, predictBBB.ai, expanding its AI ecosystem beyond oncology drug response modeling.
With a number of clinical programs, Lantern is pushing boundaries in the race to find effective cancer therapies. The company has established a wholly-owned subsidiary, Starlight Therapeutics Inc., dedicated to the clinical execution of promising treatments for CNS and brain cancers.
Read also: Unveiling Lantern Pharma's Success Story in AI-powered Precision Oncology
LP-300 is a notable investigational therapeutic targeting relapsed non-small cell lung cancer (NSCLC) in combination with standard chemotherapy. The Phase 2 HARMONIC trial has completed patient enrollment in Japan ahead of schedule, marking progress in its global expansion across the U.S., Japan, and Taiwan—regions with higher rates of never-smoker NSCLC—and aims to evaluate survival outcomes and chemotherapy response in this genetically distinct patient group. A 70-year-old never-smoker with advanced NSCLC achieved a complete response in both lung and adrenal lesions, maintaining it for nearly two years over 21 treatment cycles without dose-limiting toxicities or notable adverse events.
Another candidate, LP-284, is in a Phase 1 clinical trial focusing on relapsed or refractory lymphomas and certain solid tumors. This trial aims to determine the drug's safety, optimal dosing, and initial efficacy in a patient population with limited treatment options. In June 2024, Lantern Pharma received a Japanese patent for LP-284. In the U.S., LP-284 holds FDA Orphan Drug Designation for high-grade B-cell lymphomas and mantle cell lymphoma.
Additionally, Starlight Therapeutics, a wholly-owned subsidiary of Lantern Pharma, is advancing LP-184 (STAR-001) through a Phase 1B trial for central nervous system cancers, including glioblastomas, aiming to address a critical unmet need in brain cancer treatment. The company has additionally received FDA clearance for a Phase 1b/2 trial of LP-184 in non-small cell lung cancer patients with tumor profiles typically resistant to standard therapies.
In September 2025, Lantern reported that LP-184 successfully met all endpoints in its Phase 1a trial involving 63 patients with relapsed or refractory solid tumors, including glioblastoma. Guided by Lantern’s RADR platform, the trial established a recommended Phase 2 dose and paved the way for targeted Phase 1b/2 studies in triple-negative breast cancer, NSCLC with STK11/KEAP1 mutations, and DDR-mutated bladder cancer.
Lantern Pharma’s collaboration with Bielefeld University has produced a cryptophycin-based antibody-drug conjugate (ADC) showing picomolar potency across six solid tumor types, including breast and pancreatic cancers. The ADC, developed using cysteine-engineered antibodies and guided by Lantern’s RADR AI platform, outperformed existing MMAE-based ADCs in preclinical studies and is advancing toward IND development.
Recently, Lantern Pharma entered into a strategic AI collaboration with Oregon Therapeutics to develop a protein disulfide isomerase (PDI) inhibitor for novel cancer indications, leveraging RADR to uncover biomarkers and define combination therapies.
Lantern closed a $26.25M IPO on June 15, 2020, and as of June 30, 2025 it reported ~$15.9M in cash with runway into June 2026
Immunai
Immunai is a New-York-based artificial intelligence startup specializing in cutting-edge multiomics technology. They focus on enhancing the effectiveness of cell-based therapies and other immune-altering treatments, not just in oncology but also in a broad spectrum of inflammatory diseases. Their technology allows for the precise measurement of gene expression changes in individual cells, a capability that has broad-ranging applications in medicine and therapy design.
Recently, Immunai started a partnership with Baylor College of Medicine (BCM) to uncover the role of a new molecular target, BTG1. This finding, drawn from clinical samples of an ongoing BCM trial, promises to enhance the efficacy of T and natural killer T (NKT) cell-based cancer immunotherapies.
Their advanced single-cell RNA sequencing technology played a critical role in this discovery, demonstrating the potential of Immunai's platform to drive novel treatment options. With the revelation that targeting BTG1 could amplify CAR-NKT cells' anti-tumor function, Immunai has paved the way for the development of more effective cancer immunotherapies.
In 2021 ImmuneAI secured $215 million in a Series B funding round led by Koch Disruptive Technologies, taking its total funding to $295 million.
In September 2024, Immunai signed multi-year collaborations with AstraZeneca with reported $18M initial phase to apply its AMICA immune-cell atlas and IDE engine to trial design and biomarker work in oncology and immunology. It later entered a similar partnership with Teva focused on optimizing clinical trial performance, and added former Pfizer R&D chief Mikael Dolsten to its board to support its AI-driven programs in these areas.
This year, the company and the Parker Institute have begun building a publicly accessible single-cell/multi-omic dataset from ~1,070 checkpoint-treated patients. Following the collaboration, Immunai also launched a free single-cell immune profiling program for academic groups, offering sequencing and analysis of up to 1,000 human samples to support studies in cancer, autoimmunity, and immunotherapy.
Evaxion Biotech
Evaxion Biotech (NASDAQ: EVAX) is a public clinical-stage biotechnology company, headquartered in Copenhagen, Denmark, that specializes in developing AI-powered immunotherapies for the treatment of cancer, bacterial diseases, and viral infections. Evaxion utilizes the AI-Immunology platform, an advanced AI technology, to transform vaccine discovery and immunotherapy for infectious diseases and cancers through sophisticated computational modeling and personalized medical approaches.
In October 2025, Evaxion added an automated vaccine design module to its AI-Immunology platform, allowing the system to generate vaccine constructs by optimizing antigen sequence and conformation for maximal immune response, replacing manual design steps and reducing design time.
The AI-Immunology platform, specifically its precision oncology component, leverages the PIONEER and ObsERV systems. PIONEER accurately identifies and targets neoantigens, which are unique to cancer cells, using advanced AI models to predict T-cell responses and optimize immunotherapy. ObsERV, developed in 2023, complements this by identifying endogenous retroviruses (ERVs) as additional cancer antigens, enhancing the overall efficacy of personalized cancer treatments. This combined approach allows for the development of highly personalized and effective cancer immunotherapies with minimal adverse effects on healthy cells.
Evaxion Biotech's lead cancer vaccine candidate, EVX-01, is showing promising progress in its Phase 2 clinical trials for metastatic melanoma. Recent data presented at the American Society of Clinical Oncology (ASCO) Annual Meeting in June 2024 demonstrated that EVX-01, in combination with the anti-PD1 therapy pembrolizumab (KEYTRUDA), elicited specific and targeted immune responses in melanoma patients. These responses were characterized by the activation of both CD4+ and CD8+ T-cells, with a significant correlation between the quality of neoantigens predicted by Evaxion's AI-Immunology platform and the vaccine-induced immune responses. The vaccine was also found to be well-tolerated with only mild adverse events reported.
By January 2025, Evaxion had completed dosing all 16 patients in the Phase 2 trial of EVX-01, keeping the study on schedule for completion and data readout in the second half of the year. The trial, conducted in combination with pembrolizumab, builds on earlier one-year data showing a 69% overall response rate and strong correlation between AI-predicted neoantigens and vaccine-induced immune responses, supporting the continued evaluation of EVX-01 as a potential new treatment for advanced melanoma.
In April 2025, Evaxion presented new Phase 2 data at the AACR Annual Meeting showing that 80% of EVX-01’s vaccine targets triggered tumor-specific immune responses The following month, Evaxion began dosing patients in a one-year extension of the ongoing Phase 2 trial of EVX-01 to evaluate the vaccine’s long-term immune and clinical effects. The extension follows two years of combination therapy with pembrolizumab and will assess EVX-01 as a standalone treatment.
EVX-02, DNA-based personalized cancer immunotherapy has also shown impressive results in its Phase 1/2a study. EVX-02 in combination with the checkpoint inhibitor nivolumab, was found to be well-tolerated and all participating patients who completed the treatment were relapse-free at their last assessment.
These results have given Evaxion the confidence to fast-track EVX-03, its next-generation DNA-based personalized cancer immunotherapy, towards clinical trials. The company aims to transform these technological advancements into effective patient care.
Evaxion entered a collaboration with MSD in the autumn of 2023, which remained undisclosed until winter 2024 when the initial phases of the collaboration were successful. In September 2025, MSD licensed Evaxion’s AI-designed vaccine candidate EVX-B3, targeting a hard-to-treat infectious pathogen, and extended evaluation of EVX-B2, aimed at gonorrhea—advancing their 2023 vaccine collaboration.
Evaxion raised $10.8 million In January 2025 through a public offering of shares and warrants, with participation from MSD’s Global Health Innovation Fund and other healthcare investors. In July 2025, Evaxion finalized an agreement with the European Investment Bank to convert €3.5 million of debt into equity through a warrant purchase, immediately boosting equity by about $4.1 million.
iBio
iBio, Inc. is an innovator in the field of biotechnology, specializing in the application of artificial intelligence to develop precision antibody immunotherapies. Based in San Diego, the company utilizes a combination of its proprietary AI-based antibody optimization and mammalian display technologies to discover and develop novel therapeutic antibodies.
On June 3, 2024, iBio announced the sale of its manufacturing facility in Texas. This move eliminated $13.2 million of secured debt and marked a strategic shift towards focusing on AI-driven precision biologics.
iBio, Inc. first went public through an initial public offering (IPO) in 2008. In February 2025, iBio announced it would transfer its stock listing from the NYSE American to the Nasdaq Capital Market, with trading under the same ticker “IBIO” beginning on March 4.
The latest news from iBio is the discovery of a panel of CD3 T-cell binding antibodies. These novel antibodies are designed to bind to both T cells and tumor cells, inducing the T cells to kill the tumor cells. Earlier research into CD3-based T-cell engagers had shown promise, but was hindered by high toxicity levels and lack of cross-reactivity with non-human primates, slowing down their clinical development.
iBio has addressed these challenges using its patented epitope steering technology, directing antibodies towards specific CD3 epitopes. By combining AI-based antibody optimization with mammalian display technology, the company has widened the range of CD3 affinities, identified antibodies with cross-reactivity to non-human primates, and improved the humanness of the antibody sequences.
In March 2024, iBio entered a collaboration with AstralBio to rapidly develop novel antibodies for obesity and cardiometabolic diseases using AI-driven drug discovery techniques. Earlier in 2025, iBio and AstralBio expanded their collaboration in cardiometabolic diseases, reporting preclinical data on anti-myostatin and Activin E antibodies for obesity and muscle preservation, and finalizing a licensing deal for the Activin E program.
Topic: AI in Bio
