Top 6 AI Biotech Stocks to Watch in 2025

Top AI biotech stocks to watch in 2025 line up with a core trading principle I teach every day. Focus on catalysts you can name, risk you can size, and price action you can trade. These stocks link artificial intelligence with drug discovery in ways that can move shares fast when news hits.

Check out my complete AI stock watchlist here!

Here are the stocks at the top of my list…

6 AI Biotech Stocks to Watch in 2025

Ticker	Company	Focus	Near-Term Catalyst
NASDAQ: RXRX	Recursion Pharmaceuticals	AI drug discovery platform and partnerships	Cash runway into Q4 2027, REC-617 oncology plans, collaboration revenue updates
NASDAQ: BTAI	BioXcel Therapeutics	AI-enabled neuropsychiatry therapies	SERENITY Phase 3 success for BXCL501 and planned sNDA for at-home use
NASDAQ: ABCL	AbCellera Biologics	Antibody discovery platforms and programs	First-in-human dosing for ABCL635 and ABCL575, liquidity supports R&D
NASDAQ: SDGR	Schrödinger Inc.	Physics-based and AI software plus internal pipeline	Program changes after SGR-2921 halt, software growth, SGR-1505 readouts
NASDAQ: RLAY	Relay Therapeutics	Structure-guided, ML-enabled precision oncology	Analyst targets, clinical updates across targeted cancer programs
NASDAQ: AIFF	Firefly Neuroscience	AI brain mapping and EEG analytics	NVIDIA-powered CLEAR platform speed gains and partnership news

Before you send in your orders, take note: I have NO plans to trade these stocks unless they fit my preferred setups. This is only a watchlist.

The best traders watch more than they trade. That’s what I’m trying to model here. Pay attention to the work that goes in, not the picks that come out.

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Recursion Pharmaceuticals (NASDAQ: RXRX)

Recursion Pharmaceuticals brings AI and machine learning to drug discovery through its Recursion OS platform and a pipeline that can add real catalysts. In Q2 2025, the company reported $19.2 million in collaboration revenue, plus cash reserves of roughly $534 million that extend the cash runway into the fourth quarter of 2027. Partnerships with Roche and Genentech validate the platform approach and can smooth revenue while core programs, including REC-617 in cancer, aim for targeted clinical milestones. When I teach pattern recognition, I stress pairing platform news with liquidity and trend to frame the trade.

RXRX has shown real volatility, trading more than 50 percent below its 52-week high while the market weighs R&D spend against future sales. For short-term trades, I focus on news windows, analyst commentary, and any expansion of software licensing that diversifies revenue beyond clinical trials. Traders should track burn rate, funding options, and FDA interaction, because those headlines can move price, volume, and valuation in minutes.

BioXcel Therapeutics (NASDAQ: BTAI)

BioXcel Therapeutics is using analytics and AI tools to reposition and advance neuropsychiatry treatments with near-term label expansion potential. BXCL501 met the primary endpoint in the SERENITY Phase 3 trial for at-home agitation in bipolar disorders or schizophrenia, and the company aims to file a supplemental NDA in Q1 2026. This is a clean, tradeable setup tied to regulation, compliance, and product growth. I teach traders to stalk these calendar catalysts, then let the price confirm.

BXCL501 is already available in the U.S. under supervision as Igalmi, so an at-home label would widen the addressable patients, sales channels, and revenue leverage if approved. Shares can still whip around because the market discounts probability, not promises. I map pre-FDA ranges, watch for financing, and monitor any safety or manufacturing notes that could change risk. With biotech stocks, the first reaction is often noise. The second reaction on more data is where I look for higher probability entries.

AbCellera Biologics (NASDAQ: ABCL)

AbCellera Biologics is an antibody discovery biotech company that blends data, technology platforms, and wet-lab services to generate programs and economics. In August, the team started first-in-human studies for ABCL635 and positioned ABCL575 for Phase 1, while noting more than $750 million in available liquidity to fund development. The platform spans T-cell engagers, peptide-MHCs, and hard targets, which matters for long-term portfolio value and near-term partnership fees. I teach traders to respect cash, pipeline breadth, and clear milestones because that mix supports price into news.

ABCL’s model can produce collaboration revenue, royalties, and potentially owned clinical assets that change valuation. For trade planning, I track program starts, any FDA designations, and partner announcements that feed the top line. Price often responds to proof of progress more than promises of innovation. If data quality is strong and the company advances multiple shots on goal, you can see sustained interest even in a choppy sector.

Schrödinger Inc. (NASDAQ: SDGR)

Schrödinger combines physics-based simulation, machine learning, and SaaS to power discovery for itself and the broader pharmaceutical industry. The company discontinued SGR-2921 in AML after two patient deaths in an early study, which raises safety and platform scrutiny, yet the software business and programs like SGR-1505 still provide catalysts. Traders should note how software revenue, services, and out-licensing can offset R&D shocks to the pipeline. I teach that when a stock takes a hit, you reassess the thesis tier by tier and let the chart reset.

The key questions now are pipeline durability, partner confidence, and whether software bookings and renewals keep growing with better tools and analytics. If management leans on cash discipline and focuses on higher probability candidates, the market can reprice on cleaner stories. I watch for updated guidance, program prioritization, and any FDA interactions that clarify timelines. A measured plan, steady sales, and credible milestones help rebuild trust and price.

Relay Therapeutics (NASDAQ: RLAY)

Relay Therapeutics is building precision oncology therapies guided by structure, motion, and machine learning to target tough cancer proteins. Analysts recently issued mixed price targets, including $14 at HC Wainwright and higher targets earlier in August, which can set expectations into clinical updates. The trade here is about matching upcoming data to valuation and float. I teach traders to map levels ahead of oncology readouts and to respect how quickly markets reprice risk on trial outcomes.

Relay’s programs aim at validated oncology pathways where treatments can shift standard of care and attract pharmaceuticals partners. Stocks tied to targeted therapies often move hard on partial responses, safety, and early efficacy markers. I track cash runway, R&D costs, and any partnership funding that reduces dilution. The more credible the clinical signals and the clearer the path to FDA approval, the tighter I want my plan. News sets the tone. Price confirms the setup.

Firefly Neuroscience Inc (NASDAQ: AIFF)

Firefly Neuroscience is applying AI to EEG brain data to support diagnostics and pharmaceutical R&D with faster, cleaner analysis. The company rolled out its CLEAR platform, using NVIDIA L40S GPUs to speed preprocessing by 60 to 80 percent while improving signal quality for biomarker discovery. Shares surged more than 20 percent on August 26, 2025 as traders priced in new partnership and product opportunities in healthcare services. I teach that hardware acceleration plus proprietary data can be a real catalyst when it shortens time to insights.

The story rests on data scale, machine learning accuracy, and the ability to translate analytics into products or clinical tools that drive revenue. Traders should watch for funded studies, contracts with health systems, and any steps toward regulatory pathways for diagnostics. With a high price to sales and negative profitability, funding and costs matter. I plan trades around official news, volume, and whether buyers defend prior breakouts after the first headline spike.

Catalysts and Growth Drivers in the Biotech Industry

Catalysts and growth drivers in the biotech industry are lining up for companies that fuse AI with drug discovery and development. The first driver is time. Faster, cheaper analysis across biology, chemistry, and clinical data can lower R&D costs and shorten cycles from hit finding to human studies. Traders want clear event paths across the pipeline, from INDs to Phase 2 signals. I teach that a defined calendar with real information flow gives you repeatable setups.

Check out my list of the top biotech penny stocks here!

Another driver is data. Genomic sequencing, proteomics, and imaging create massive datasets that machine learning can analyze for new targets, better patient selection, and smarter trial designs. Pair that with precision medicine and you get therapies, treatments, and services tuned to subgroups that can improve outcomes and sales per patient. Add in FDA breakthrough designations and fast track statuses that can pull forward timelines. When regulation speeds up and analytics improve, stocks can re-rate as investors update growth, valuation, and revenue models.

Demand for Faster, Cost-Effective Drug Development

Demand for faster, cost-effective drug development is the core reason AI tools and platforms are winning attention across the sector. Automation, high-throughput screens, and data analytics can reduce the number of failed experiments and compress timelines from discovery to clinical trials. That changes the unit economics of research, development, and manufacturing for both biotech companies and pharmaceuticals partners. In my teaching, I tell traders to follow cost signals because lower burn extends runway and supports stronger price action into catalysts.

On the street, this shows up as partnerships that fund programs, services that monetize software, and milestones that de-risk the path to approval. Investors notice when companies move from pure R&D to revenue streams linked to platform licensing or discovery deals. Those contracts validate technology and can improve market confidence. For trades, I watch quarter to quarter updates on spend, cash, and program counts. Efficiency is not a headline by itself, but it powers the headlines that move stocks.

Rising Interest in AI-Enabled Precision Medicine

Rising interest in AI-enabled precision medicine is pushing companies to match drugs with the right patients using better data. Machine learning on genomics, proteomics, and clinical history can refine trial enrollment, cut adverse events, and improve response rates. That supports stronger efficacy signals and a cleaner story for payers and regulators. I teach that when a company narrows its target population with clear biomarkers, the market often rewards the focus.

The upside is not just medical. Precision medicine can improve revenue per patient, optimize pricing, and support value-based contracts in healthcare. It can also open lines to companion diagnostics that add products and services to the portfolio. For traders, that means more shots on goal and more newsworthy milestones. Watch for data on patient stratification, tumor profiling in oncology, and early talks with the FDA about companion tests. Clear links between biomarkers and outcomes can be the difference between a trade and a trend.

Advances in Genomic and Molecular Data Analysis

Advances in genomic and molecular data analysis are giving AI more signal to work with. Single-cell sequencing, spatial biology, and protein structure prediction add context that improves target selection and drug design. When platforms combine wet-lab automation with software, they can run more experiments and generate higher quality data per dollar. I teach that quality data is an edge, and edges show up in price when the street trusts the information.

These advances also improve hit triage, lead optimization, and toxicity prediction, which can limit costly failures in animal studies and human trials. The downstream impact is better pipeline productivity and more credible timelines to revenue. Stocks that report clean mechanistic evidence, reproducible results, and consistent analysis often build support on pullbacks. I track how companies talk about their data, whether they release peer-reviewed research, and how partners commit funds. Trust is earned with methods, not just marketing.

Regulatory Approvals and Breakthrough Designations

Regulatory approvals and breakthrough designations can turn development projects into products. Fast Track, Breakthrough Therapy, and Priority Review are labels that signal the FDA sees potential value for patients and is willing to accelerate review. That tells the market to adjust expectations on time to approval, sales ramp, and profitability. In my classes, I stress that regulation is a calendar you can trade if you respect both upside and risk.

For AI biotech stocks, a smart regulatory plan can be as valuable as a new dataset. Early meetings with the FDA, clean safety profiles, and strong endpoints help avoid delays that burn cash and confidence. Traders should follow press releases, advisory committee dates, and any new guidance that shifts the rules. When approval odds rise, valuation can move sharply as funds rebalance portfolios. When setbacks hit, price can retrace just as fast. Have a plan for both outcomes before the news prints.

Risks and Considerations Associated With AI Biotech Stocks

Investment in AI-driven healthcare and biotech companies carries unique risks that traders must account for, even when the technology looks promising. High burn rates are common because research, clinical trials, and platform buildout require capital long before revenue shows up. That leads to funding events, share issuance, and valuation swings. I teach risk first. You can always reenter a strong trend after supply is absorbed.

Outcomes depend on clinical trial data that can change a company’s path overnight. A positive read can unlock partnerships and revenue. A miss can force program cuts and new funding. Regulatory events add volatility when approvals, policy shifts, or public health news ripple through the market. Finally, intellectual property and data quality matter because algorithms, datasets, and patents protect profits. Traders should track patents, data integrity, and compliance disclosures. If the science, rights, or records are shaky, the trade thesis is weaker than the chart suggests.

What Is the Future Outlook for AI Biotech Stocks?

The future outlook for AI biotech stocks is shaped by platform value, clinical proof, and sustainable funding. Companies that turn data into better decisions will attract partnerships that bring revenue, services, and co-development dollars. When those programs hit endpoints and secure FDA approvals, you get products and cash flows. That transition is where large funds step in. In my teaching, I focus on finding the first clean signal that the story has turned from promise to performance.

Expect more collaboration between software leaders and pharmaceutical R&D units that need better tools. Expect more precision medicine strategies in oncology and rare disease where sample sizes are small and analytics matter. Traders should prepare for faster news cycles and sharper moves as markets react to analysis, insights, and information in real time. If you plan around catalysts, respect stops, and stick to your process, this sector can offer repeatable opportunity and growth.

Is Now the Right Time to Buy AI Biotech Stocks?

Now may be the right time to trade AI biotech stocks if you can frame entries around catalysts, manage risk, and avoid chasing. The sector often trades on news, not just numbers, so align your plan to data readouts, funding updates, and regulatory decisions. I teach that you want liquidity, a defined thesis, and a risk level you can live with. If those three boxes are checked, you have a trade. If not, you wait.

You do not need to predict the market. You need to prepare. Build a watchlist, note price levels where buyers showed up, and write down the next event for each name. Keep position sizes reasonable because R&D and FDA headlines can gap stocks beyond comfort. If the chart and the calendar line up, take the trade with a stop. If they don’t, protect capital and move to the next setup. There is always another stock.

Key Takeaways

• AI in biotech is about speed, data quality, and smarter decisions that reduce R&D costs and improve trial outcomes. Stocks move when those advantages show up in real programs, real patients, and real revenue, whether from services, licensing, or approved products
• Risk is not optional. High burn, trial dependence, and regulation create sharp reactions to news. Plan for both directions and let the price confirm.
• Trade the plan, not the hype. Your edge is preparation.

This is a market tailor-made for traders who are prepared. AI stocks thrive on volatility, but it’s up to you to capitalize on it. Stick to your plan, manage your risk, and don’t let FOMO drive your decisions.

These opportunities are fast and unpredictable, but with the right strategy, you can make them work for you.

If you want to know what I’m looking for — check out my free webinar here!

Frequently Asked Questions

How Is AI Transforming the Biotech and Pharmaceutical Industries?

AI is transforming the biotech and pharmaceutical industries by turning raw data into better drug discovery and development decisions. Machine learning tools analyze genomics, imaging, chemistry, and clinical records to prioritize targets, design molecules, and predict safety. That reduces wasted experiments and shortens timelines from lab to patients. In trading terms, it creates more frequent and higher quality catalysts across the pipeline. I teach traders to follow where the data gets monetized, because that is where price tends to run.

This shift also boosts services and software revenue as platforms license tools to pharma R&D. Companion diagnostics, precision medicine, and smarter trial designs raise the odds of success and can support pricing and profitability after approval. For traders, the signal is in partnerships, FDA designations, and repeat contract wins. When data improves outcomes and cuts costs, companies earn attention from investors, which can translate to stronger performance and more liquid setups.

What Makes AI Biotech Stocks More Volatile Than Traditional Biotech?

AI biotech stocks can be more volatile than traditional biotech because they carry two sets of expectations. The market prices both the science and the software. If a platform claims faster discovery and better trial design, misses can trigger faster repricing. That is why I teach traders to size positions with respect for gap risk and to plan exits before the news. Volatility is a feature here, not a bug.

These companies often have higher R&D spend on both technology and therapeutics, which adds funding risk. They also face questions about data quality, model validity, and real-world impact. When partnerships or clinical trials validate claims, price can spike. When a program is halted or a deal slips, price can slide. The trade is to map catalysts, watch liquidity, and be ready to shift bias as information changes. Process beats prediction.

How Do I Evaluate the Strength of an AI Biotech Platform?

Evaluate the strength of an AI biotech platform by tracking outcomes, not slogans. Start with published research, peer-reviewed methods, and reproducible results. Add customer signals like multi-year pharma deals, milestone payments, and expansion across programs. Then look at how the platform impacted timelines, costs, and success rates in real clinical trials. I teach that you want proof the tools changed decisions that matter. That is tradeable.

Also check the data foundation. Size, quality, and control over datasets can protect an edge and support intellectual property. Review cash runway, R&D plans, and how the company turns tools into products or services with revenue. Finally, watch regulatory steps, including FDA interactions that show a credible path to approval. If the platform improves patient selection, reduces toxicity, or boosts efficacy, that evidence will show up in trial design and endpoints. That is when I get more interested.

Are There ETFs That Include AI Biotech Stocks?

There are ETFs that include AI biotech stocks, often inside broader healthcare, biotech, or technology funds that hold platform names and pharmaceuticals partners. Some funds track biotech indices with exposure to drug discovery software, precision medicine, and clinical-stage companies. Others focus on artificial intelligence and include healthcare technology providers with analytics and services revenue. I teach traders to check holdings, weights, and liquidity before trading an ETF for exposure.

ETFs can smooth single-name risk from clinical trials or FDA events, but they also mute upside from a big win. If your edge is trading catalysts with clear dates, single stocks may offer better reward with tighter plans. If you want sector exposure while you learn the setups, an ETF can work. Always review fees, average volume, and recent performance against your goals. Align the vehicle with your strategy, not the other way around.

How Do Venture Capital Flows Impact Potential Returns in AI Biotech Stocks?

Venture capital can extend the runway for R&D and the pipeline, but it also shapes valuation, dilution, and the pace of clinical trials that drive tradeable news. When top investors lead a funding round, it can attract partnerships and analysts, which improves liquidity and can boost potential returns if milestones hit on time. I track round size, terms, and syndicate quality because strong funding often precedes catalysts in this sector and weak funding can raise risk across a portfolio.