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The pharmaceutical industry in 2025 is changing like never before, with traditional methodologies being redefined by technology and collaborative approaches. Historically, the drug discovery process started with extensive screening of chemical libraries to identify therapeutic candidates or de-novo drug discovery where new chemical molecules are designed based on the molecular target. Typically, researchers at pharmaceutical companies conduct high-throughput screening and mass spectrometry to test thousands of small molecules for activity against known disease targets.
However, the industry is now focusing more on targeted strategies, emphasizing rare diseases and personalized medicine. The biological complexity of these conditions demands better knowledge and tailored therapeutic solutions.
Gaurav Kapoor, President of Newristics, aptly notes,
"Discovering a new drug is becoming increasingly complicated because most companies are focusing on rare diseases, which are not easy to treat."
This shift shows the need for new ways to deal with the difficulties of modern drug discovery.
With over 7,000 known rare diseases affecting an estimated 400 million people globally, the focus on targeted therapeutics for rare diseases is not surprising. The orphan drug market is projected to surpass $394.7 billion by 2030.The orphan drug market is projected to cross $394.7 billion by 2030.
However, rare disease drug development comes with substantial hurdles. These conditions often are poorly understood from a basic biology standpoint, requiring a lot of deep foundational research to identify relevant molecular pathways and therapeutic targets.
Gaurav Kapoor explains:
"Translational research is very expensive and majority of pharma companies cannot afford to do it on their own."
To cross this bridge, pharmaceutical companies are increasingly partnering with academic institutions to gain access to cutting-edge research. This explains the R&D hubs forming around institutions like MIT and Harvard in Cambridge and Boston, enabling direct collaboration with top scientific minds. The academia-industry partnership is proving to be fruitful in developing new treatments for genetic, autoimmune, and oncology-based rare conditions, turning scientific insights into viable drug candidates.
Modern drug discovery is inherently multidisciplinary. It brings together biologists, chemists, physicists, data scientists, geneticists, and computational modelers to solve complex biomedical puzzles. With most rare and specialty diseases rooted in genetic anomalies, large-scale genetic databases and supercomputing help in identifying promising drug targets.
Yet, this level of sophistication comes at a cost. According to a 2025 RAND Corporation analysis, median R&D costs for novel drugs hover around $150 million, with many exceeding $1.3 billion depending on the therapeutic area and trial complexity.
To offset this, companies are adopting collaborative and risk-sharing models.
Kapoor notes:
"Pharma is not only collaborating with academic institutions but also creating co-promotion and co-marketing agreements."
Joint ventures between former competitors, shared funding pools, and cross-licensing deals are becoming the norm—all in an effort to de-risk innovation and expedite access to life-saving treatments. These partnerships also help accelerate regulatory approval by pooling clinical and scientific data.
The COVID-19 pandemic served as a global case study in rapid pharmaceutical mobilization. What once took 7–10 years—from drug discovery to FDA approval—was accomplished in under a year for multiple vaccines and antivirals.
This compressed timeline, made possible by AI-powered modeling, real-time data sharing, and adaptive trial designs, has inspired a new gold standard. Collaborative infrastructure, emergency funding, and rolling regulatory reviews played
important roles.
Some oncology drugs are now reaching market in as little as 2–3 years, driven by faster feasibility assessments, better trial matching, and platform-based clinical operations. This accelerated pace is now being applied to other therapeutic areas where unmet need is high.
The industry in 2025 is focused on applying this "pandemic pace" to non-COVID therapies, balancing speed with safety and long-term efficacy.
Like every other field, AI is already changing drug discovery and will be omnipresent in every step of the drug discovery pipeline in the very near future. From hypothesis generation to lead optimization, machine learning algorithms are taking years off traditional timelines and uncovering previously hidden patterns in massive datasets.
DeepMind's AlphaFold, for instance, made headlines by predicting protein folding with unprecedented accuracy—a breakthrough that addresses one of biology's greatest challenges. This helps researchers understand complex biological pathways that were previously opaque and accelerates the identification of viable drug targets.
"Advances in deep learning algorithms at DeepMind and other AI companies hold significant promise for drug discovery in the future," says Gaurav Kapoor.
Other AI-powered platforms are now capable of designing drug candidates from scratch, identifying repurposing opportunities, and reducing trial-and-error in compound testing.
One notable example: Insilico Medicine's AI-generated anti-fibrotic drug, which entered Phase 2 trials in record time. These developments highlight AI's growing role in democratizing access to drug innovation and drug launch.
One of the most groundbreaking uses of AI lies in virtual clinical trial simulations. By leveraging massive datasets of patient characteristics, AI models can simulate outcomes for thousands of hypothetical patients, providing predictive insights before a single trial begins.
Kapoor explains:
"AI models can simulate thousands of different patients and predict their response to any drug with reasonable accuracy. These simulations inform dosage adjustments, identify high-risk subpopulations, and even determine whether a drug is likely to fail, saving companies millions in development costs.”
Adaptive clinical trials with AI driving its optimization can now change protocols in real-time based on interim results, making trials more efficient, ethical, and patient-centric.
The future of drug discovery in 2025 includes ecosystems of smart diagnostics, wearable tech, and companion apps. These tools not only enhance treatment efficacy but also bolster real-world evidence collection for regulators and payers.
"Pharmaceutical companies are realizing that the patent life of a molecule can be extended by integrating such technologies," says Gaurav Kapoor.
From biomarker-based diagnostics to connected inhalers and infusion monitors, these innovations provide added value to payers and patients alike—a critical differentiator in an era of reimbursement pressures. In many cases, the drug-device combination becomes a strategic asset for market access. Explore how AI is transforming the life sciences industry, enhancing drug discovery and HCP marketing, while driving efficiency and innovation for pharmaceutical marketers.
Drug discovery is now a dynamic, interdisciplinary, and data-driven endeavor. By embracing AI, fostering cross-sector collaboration, and adopting agile development models, pharma companies in 2025 can navigate the complexities of modern therapeutics.
As the landscape continues to evolve, Newristics remains committed to supporting pharma leaders with behavioral science-driven solutions that enhance market strategy, optimize messaging, and accelerate adoption.
Explore how Newristics can help you lead in the age of smart drug discovery. Learn more.
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Newristics
01 Feb 2024
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