Drug Discovery AI Buyer’s Guide for Research & Development

Who This Guide Is For

You need this guide if you are one of these buyers:

• VP of R&D / Head of Discovery
• You need faster target-to-hit cycles, better target prioritization, and fewer dead-end programs.
• Your pain: too many hypotheses, not enough validated shots on goal, and wet-lab capacity that is always the bottleneck.

• Director of Computational Chemistry / Computational Biology
• You need software that actually fits your modeling stack, not a glossy demo.
• Your pain: fragmented data, model drift, poor reproducibility, and tools that work in a notebook but not in production.

• Head of Translational Science / Precision Medicine
• You need platforms that connect omics, clinical, and biomarker data to actionable decisions.
• Your pain: data silos, weak cohort stratification, and too much manual analysis.

• Procurement / Innovation / Strategic Sourcing in Pharma or Biotech
• You need to compare vendors that sell very different things under the same “AI drug discovery” label.
• Your pain: every demo sounds transformational, but the contracts, data rights, and real deliverables are wildly different.

You should not read this guide if:

• You are only looking for generic data science tooling with no drug-discovery-specific workflow.
• You need a simple lab inventory or ELN/LIMS replacement.
• You want a cheap point solution and do not care about scientific rigor, data rights, or enterprise integration.
• You are not willing to support the effort with clean assay data, chemoinformatics discipline, and wet-lab validation.

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What Drug Discovery AI Software Does

Drug discovery AI software is not one market. It is a bundle of different capabilities used at different stages of R&D:

• Target discovery
• Hit finding / virtual screening
• De novo molecule generation
• Lead optimization
• Biologics / antibody design
• Translational and clinical prediction
• Knowledge graph / literature intelligence
• Data orchestration for multimodal R&D

The biggest buyer mistake is assuming all of these are interchangeable. They are not.

Before vs. After: What changes when AI is actually used well

What this software really buys you

At a business level, Drug Discovery AI should do at least one of these:

• Reduce time to candidate nomination
• Increase probability of technical success
• Reduce wet-lab spend on low-value experiments
• Improve target quality and mechanistic confidence
• Improve translational prediction and trial readiness

Concrete dollar-value example

Suppose a mid-sized biotech runs 4 discovery programs a year, each burning:

• $6M in discovery spend before candidate nomination
• 18 months to get to a preclinical candidate
• 2–3 major synthesis/assay cycles per program after hitting a dead end

If an AI platform helps cut even 20% of wasted synthesis and assay work, and reduces cycle time by 3 months, the value can be real:

• Lab spend saved: 20% of $6M = $1.2M per program
• Time value: 3 months earlier nomination can be worth millions in portfolio NPV, especially in oncology or rare disease
• Portfolio effect: if only 1 of 4 programs is rescued or upgraded, the platform can pay for itself fast

But here is the blunt truth: If you do not have clean assay data, reproducible biology, and a team that knows how to validate predictions, AI will mostly produce faster nonsense.

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Market Landscape 2026

The 2026 Drug Discovery AI market is crowded, hyped, and bifurcated.

The market is splitting into 6 vendor types

What AI changed by 2026

The market is no longer about “can AI find a molecule?” That question is mostly stale. The real questions are:

• Can the platform consistently improve hit rates?
• Can it work on your data, not just benchmark datasets?
• Can it fit into your existing R&D operating model?
• Can it produce defensible results that scientists trust?
• Can it support regulatory, IP, and data-governance needs?

Major trends you need to know

1) Physics + ML is beating pure black-box hype

Tools like Schrödinger Maestro, XtalPi’s XFEP/XGlue/XMolGen, AtomNet, and NeuralPLexer reflect a market reality: for drug discovery, mechanistic modeling still matters. Pure generative models without physics or assay grounding often look impressive and fail in the lab.

2) End-to-end platforms are consolidating value

Vendors like Insilico Medicine, Recursion Pharmaceuticals, Insitro, BenevolentAI, Isomorphic Labs, and Valo Health are positioning around integrated discovery stacks. This makes sense for enterprise buyers, but only if you have the internal capacity to absorb them.

3) Biologics are a major growth zone

Protein, antibody, and enzyme design is where AI has the clearest near-term commercial pull. That is why Absci, Generate Biomedicines, LabGenius, Benchling’s PipeBio Antibodies, and Ginkgo Bioworks matter.

4) Clinical and precision medicine data are becoming discovery inputs

Tempus AI, Paige, and others are expanding the discovery funnel by linking molecular and real-world clinical data. This is useful, but don’t confuse trial matching with drug discovery.

5) Partnerships are still the dominant monetization model

Most of the serious players are not selling $99/month SaaS. They are selling:

• enterprise licenses
• multi-year partnerships
• milestone-based collaborations
• revenue sharing
• royalties
• internal-platform access for sponsored programs

That matters because procurement needs to negotiate IP rights, publication rights, data rights, and success metrics, not just subscription fees.

Where the 61 products fit

Here is how the directory maps to the market:

• AI-native discovery engines: Absci Drug Creation, AtomNet, BenevolentAI Platform, Generate Platform, Pharma.AI, Insitro Platform, Isomorphic Labs Platform, Recursion OS, Relay Platform, Valo Opal
• Molecule/chemistry design: Chemistry42, Maestro, Makya, DeepCure Platform, GEMS, XFEP, XGlue, XMolGen, NeuralPLexer
• Biologics / antibody / protein design: Absci, Generate Platform, EVA Platform, PipeBio Antibodies, AI Target Discovery by Absci, AI Models and Fine-Tuning Solutions by Ginkgo Bioworks
• Knowledge and data intelligence: Cortellis, DRG Fusion, PandaOmics, TargetBench, TargetPro, PhenAID, Geneious Biologics
• Translational / clinical / patient data: Tempus ONE, Trials, Paige Predict, Tempus ECG-Low EF Software
• Services and hybrid R&D execution: Evotec AI Platform, Drug Discovery & Pre-clinical Development Services, Cyprotex, EVOcells, iPSC-based disease modeling, PanOmics, Molecular Patient Databases, Just - Evotec Biologics, Aurigene.AI, AIDDISSON™

The blunt market view

If a vendor cannot show one of the following, be skeptical:

• prospective evidence
• internal validation on your assay class
• measurable enrichment over baseline
• reproducible wet-lab hit rate improvements
• clear rights to models, data, and outputs

A demo is not evidence. A benchmark is not your biology. A case study with undisclosed baseline is marketing.

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The 9 Things That Matter Most

1) Scientific validity and evidence quality — 18%

Why it’s critical: If the model is not scientifically grounded, everything downstream is noise.

What to evaluate:

• Prospective validation, not just retrospective benchmarks
• Assay relevance to your disease area
• Train/test leakage risks
• External validation across targets and modalities
• Whether the vendor publishes methods, not just outcomes

What good looks like:

• Demonstrated uplift in hit rate, lead quality, or prioritization accuracy
• Clear separation between training data and evaluation data
• Results replicated on unseen projects
• Evidence in peer-reviewed or rigorously documented settings

Red flag: “Industry-leading accuracy” with no target-class specificity and no baseline.

Ask the vendor:

• What was the exact baseline comparator?
• Was the evaluation prospective or retrospective?
• Can you show performance on targets similar to ours?

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2) Workflow fit — 15%

Why it’s critical: A brilliant model that does not fit your discovery workflow will die in pilot.

What to evaluate:

• Where it plugs in: target discovery, design, optimization, translation
• Whether it supports your current stage of R&D
• Whether it requires a new operating model
• How much scientist time it adds vs. removes

What good looks like:

• Fits into existing decision gates
• Supports handoff between computational and wet-lab teams
• Reduces manual data wrangling
• Produces outputs scientists can act on immediately

Red flag: The platform forces your team to reorganize around vendor logic.

Ask the vendor:

• Which workflow step changes the most?
• What tasks will my scientists do differently next week?
• How many hours per project does the platform usually save?

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3) Data readiness and integration depth — 15%

Why it’s critical: Most discovery teams underestimate how messy their data is.

What to evaluate:

• LIMS, ELN, assay, omics, imaging, and clinical integration
• API quality and data model flexibility
• Support for historical and streaming data
• Metadata handling and provenance tracking

What good looks like:

• Can ingest messy real-world R&D data
• Handles multi-modal inputs cleanly
• Preserves provenance and versioning
• Has documented integrations or strong APIs

Red flag: Only works with clean, curated toy datasets or manual CSV uploads.

Ask the vendor:

• What data formats do you support natively?
• How do you handle missing values and inconsistent metadata?
• How long does data onboarding usually take?

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4) Model transparency and interpretability — 12%

Why it’s critical: Scientists need to trust decisions, not just accept scores.

What to evaluate:

• Explainability of outputs
• Ability to inspect feature importance or mechanistic drivers
• Uncertainty estimates
• Audit trails and reproducibility

What good looks like:

• Rankings come with reasons
• Confidence intervals or uncertainty measures
• Scientist can interrogate assumptions
• Clear version control for model runs

Red flag: A black box that outputs a list and says “trust the model.”

Ask the vendor:

• How do users understand why a candidate was ranked highly?
• Can we reproduce the same result later?
• How are uncertainty and confidence communicated?

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5) Biological scope and modality coverage — 10%

Why it’s critical: Some tools are excellent for one modality and poor for others.

What to evaluate:

• Small molecules vs biologics vs proteins vs antibodies
• Target discovery vs molecule generation vs clinical prediction
• Whether the vendor is truly multi-modal or just marketing as such

What good looks like:

• Strong depth in your core modality
• No fake “platform” claims
• Clear limits on where the model works best

Red flag: A vendor claims to do everything equally well.

Ask the vendor:

• Where do you consistently outperform specialist tools?
• Which modality is your strongest?
• Where do customers usually stop using the platform?

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6) IP rights, data rights, and confidentiality — 10%

Why it’s critical: In drug discovery, the output is only as valuable as the rights you keep.

What to evaluate:

• Ownership of model outputs, prompts, generated candidates, and downstream data
• Training rights on your data
• Publication controls
• Partitioning of your data from other customers

What good looks like:

• You keep rights to your project outputs
• Clear restrictions on vendor re-use
• Strong contractual language around confidentiality
• Explicit stance on model training with customer data

Red flag: Vague IP language or “vendor may use de-identified data to improve services” without limits.

Ask the vendor:

• Do we own all generated structures, annotations, and scores?
• Can you train on our data without explicit opt-in?
• What happens to our data when the contract ends?

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7) Implementation effort and change management — 10%

Why it’s critical: Most failed deployments fail because teams underestimate operational lift.

What to evaluate:

• Onboarding timeline
• Internal resource needs
• Data cleanup burden
• Training requirements for scientists and IT

What good looks like:

• Clear implementation plan
• Named customer success resources
• Pilot-to-production path
• Documented training materials and playbooks

Red flag: “Go-live in 2 weeks” for a platform that needs real data integration.

Ask the vendor:

• What internal roles do we need to dedicate?
• What is the typical implementation timeline by customer size?
• What are the most common onboarding delays?

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8) Evidence of operational value — 5%

Why it’s critical: A model can be elegant and still not improve R&D economics.

What to evaluate:

• Hit rates
• Cycle time reduction
• Cost per decision
• Number of candidates advanced
• Reduction in failed experiments

What good looks like:

• Measurable business outcomes, not just technical metrics
• Clear before/after comparisons
• Economic value tied to milestones

Red flag: Only AUC, precision, recall, or benchmark scores with no R&D outcome metrics.

Ask the vendor:

• How do customers measure ROI after 6 and 12 months?
• What operational KPI improves most often?
• What percentage of pilots convert to production?

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9) Vendor viability and partnership model — 5%

Why it’s critical: Some of these vendors are platform companies; others are research organizations disguised as software companies.

What to evaluate:

• Financial stability
• Focus of roadmap
• Whether software is core or secondary
• Partnership history and renewal rates

What good looks like:

• Clear product roadmap
• Evidence of repeated customer adoption
• Support that survives leadership changes
• Stable commercialization strategy

Red flag: A vendor that depends entirely on one or two giant partnerships.

Ask the vendor:

• What percentage of revenue comes from software vs collaborations?
• How many customers renew after the first term?
• What is the roadmap for the next 12 months?

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Use-Case Matching: Which Type of Buyer Are You?

1) Growth-stage biotech

What matters most:

• Speed
• Focus
• Limited integration burden
• Clear ROI within one program

Prioritize:

• Chemistry42
• AtomNet
• Maestro
• PipeBio Antibodies
• Absci Drug Creation
• DeepCure Platform

What to avoid:

• Giant enterprise suites that require 9 months of onboarding
• Platforms that demand a full data warehouse rewrite

Best fit: Pointed use cases where one discovery bottleneck is killing velocity.

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2) Large pharma / enterprise R&D

What matters most:

• Multi-program scalability
• Governance
• IP protection
• Integration with existing informatics stack

Prioritize:

• Pharma.AI
• BenevolentAI Platform
• Recursion OS
• Isomorphic Labs Platform
• Cortellis
• DRG Fusion
• Valo Opal
• Insitro Platform

What to avoid:

• Narrow tools that solve one task but cannot scale across teams
• Vendors that cannot support enterprise security and contracting

Best fit: Organizations that can absorb a platform and run multiple pilots in parallel.

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3) Biologics-heavy organization

What matters most:

• Protein/antibody design
• Developability
• Manufacturability
• Sequence-function learning

Prioritize:

• Absci Drug Creation
• Generate Platform
• EVA Platform
• PipeBio Antibodies
• Ginkgo Bioworks AI Models and Fine-Tuning Solutions
• AI Target Discovery by Absci

What to avoid:

• Small-molecule-centric tools presented as “multimodal”
• Black-box generation without biophysical or developability constraints

Best fit: Teams building therapeutic proteins, antibodies, or engineered biologics.

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4) Budget-conscious or validation-first buyer

What matters most:

• Proof before commitment
• Modest implementation effort
• Flexible commercial terms
• Fast learning

Prioritize:

• Maestro
• Cortellis
• DORA
• Chemistry42
• PhenAID
• TargetBench
• TargetPro

What to avoid:

• Multi-year partnerships before a pilot proves value
• Vendors that charge enterprise pricing for exploratory use

Best fit: Teams that need to validate whether AI adds value before scaling spend.

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Vendor Evaluation Questions

Features

• Which exact stage of discovery do you support best?
• Do you focus on small molecules, biologics, or both?
• Can the platform handle target discovery, candidate design, and optimization end-to-end?
• What outputs do users receive: ranked list, structure proposals, explanations, uncertainty?
• Can we export all results and metadata in usable formats?
• What model types do you use: physics-based, graph ML, transformers, knowledge graph, generative models?
• How do you prevent train/test leakage?
• What evidence shows improvement over baseline methods?

Integration

• What data sources can you ingest natively?
• Do you integrate with ELN, LIMS, assay systems, omics platforms, EHR, or clinical systems?
• What APIs are available, and are they documented?
• Can we connect via SSO and role-based access control?
• How do you handle data provenance and versioning?
• Can the platform run in our cloud or private environment?
• What is the typical time to onboard historical datasets?
• Do you support batch, streaming, or both?

Security and Compliance

• What security certifications do you hold?
• How is customer data isolated from other customers?
• Do you train your models on customer data by default?
• What is your data retention and deletion policy?
• Who owns generated outputs and model-derived artifacts?
• How do you manage access logs and audit trails?
• Do you support regulated workflows or GxP-adjacent use cases?

Pricing and Commercials

• What is the pricing model: seat-based, usage-based, subscription, partnership, milestone-based?
• What are the implementation fees?
• What is included in the base contract vs professional services?
• Are there minimum commitments?
• What happens if we reduce usage mid-contract?
• Are pilot results convertible into production pricing?
• What commercial terms apply to generated molecules, targets, or biomarkers?
• Can we get a short pilot with a clear exit option?

Implementation and Support

• What internal resources do you need from us?
• What does a typical 6-week pilot look like?
• Who will support us day to day?
• What are the most common causes of implementation failure?
• How do you train scientists who are not computational experts?
• What is your support SLA?
• How often do you release model or platform updates?
• How do you handle validation after model updates?

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Common Mistakes to Avoid

Mistake #1: Buying the coolest demo

A slick interface is not proof of scientific value. Vendors can make a demo look magical with curated data and hand-holding. Consequence: You buy a tool nobody trusts. Do instead: Demand evidence on your target class or a close analog.

Mistake #2: Treating all AI drug discovery tools as the same

A target-discovery platform, a molecular design engine, and a clinical cohort tool solve different problems. Consequence: You pick a tool that solves the wrong bottleneck. Do instead: Start with the exact workflow constraint you need to remove.

Mistake #3: Ignoring data readiness

If your assay data is messy, unlabeled, or inconsistent, the platform will underperform. Consequence: Long onboarding, weak results, blame-shifting. Do instead: Budget 2–4 weeks for data cleanup before pilot kickoff, longer if your data is spread across teams.

Mistake #4: Overweighting benchmark metrics

AUC and top-k scores are useful, but they do not tell you whether the model improves decision quality in your program. Consequence: You optimize the wrong metric. Do instead: Ask for prospective hit rates, cycle time reduction, and candidate quality metrics.

Mistake #5: Underestimating IP and data-rights complexity

Some contracts are vague on output ownership, data reuse, and model training rights. Consequence: You create legal risk or lose leverage later. Do instead: Put IP terms in writing before pilot start.

Mistake #6: Expecting AI to replace wet-lab validation

No serious discovery workflow eliminates experimental confirmation. Consequence: False confidence and wasted experiments. Do instead: Use AI to narrow the search space, then validate aggressively.

Mistake #7: Buying enterprise software without an operating model

If no one owns usage, governance, or output review, the platform becomes shelfware. Consequence: Adoption collapses after the pilot. Do instead: Assign clear scientific ownership and a decision gate for every output.

Mistake #8: Letting services masquerade as product

Some vendors are really selling consulting, not software. That can be fine — if you know that upfront. Consequence: You pay platform prices for custom work. Do instead: Separate product fees from service fees and define deliverables precisely.

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Evaluation Timeline & Process

Recommended 8-week evaluation timeline

Weighted evaluation matrix template

Scoring guidance

• 5 = Excellent fit
• 4 = Good fit with manageable gaps
• 3 = Acceptable but risky
• 2 = Weak fit
• 1 = Do not proceed

Do not average away major risks. If IP is weak or the science is not credible, a high UI score does not matter.

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Implementation Reality Check

Typical timeline by company size

Key success factors

• One clear use case
• High-quality historical data
• Scientific owner who has decision power
• Fast feedback loop with wet-lab validation
• Realistic expectations on model performance
• Explicit contract terms on IP and data reuse

What vendors won’t tell you

• The first dataset usually needs cleanup
• Your internal team will do more work than the demo implies
• Model performance may drop when moved from curated to messy production data
• The biggest value often comes from workflow triage, not model brilliance
• Enterprise procurement can easily add 2–4 months
• Success depends more on adoption than algorithm choice

Reality check by use case

• Target discovery: Expect the best value when biology is complex and existing hypotheses are weak.
• Small-molecule design: Strongest when paired with a good structural biology and medicinal chemistry team.
• Biologics: Most useful where sequence design space is large and early developability filtering matters.
• Clinical prediction / trial matching: Useful when multimodal data is available and patient cohorts are large enough.

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Red Flags — When to Walk Away

Walk away if you hit two or more of these hard red flags and the vendor cannot fix them in writing.

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How This Guide Was Created

This guide was created using a vendor-neutral synthesis of product positioning, category structure, and buyer evaluation logic across the Drug Discovery AI market.

• Review date: 2026-09-09
• Update frequency: Quarterly, or sooner if major platform changes, acquisitions, or new product launches materially alter the market
• Independence disclaimer: commercial.ai is an independent directory. This guide is written to help buyers evaluate the category objectively, not to promote any single vendor or commercial relationship.

If you are buying Drug Discovery AI software, the right question is not “Which vendor has the best AI?” It is: Which platform will improve our specific discovery decision, on our data, within our operating model, with rights we can defend?

Drug Discovery AI solutions in the catalogue (52)

• Absci Drug Creation by Absci — Unlocking new drug candidates faster by integrating AI with proprietary wet-lab data.
• AI Models and Fine-Tuning Solutions by Ginkgo Bioworks — AI models and fine-tuning solutions for biological research, including protein language and promoter activity models.
• AI Target Discovery by Absci — Leveraging reverse immunology and generative AI to discover novel therapeutic targets.
• AlphaFold 3 by Isomorphic Labs — Predicting the structures and interactions of all of life's molecules with unprecedented accuracy.
• AtomNet by Atomwise — Deep-learning virtual screening to find novel small-molecule hits faster
• BenevolentAI Platform by BenevolentAI — AI-driven drug discovery: accelerating science, transforming medicine.
• Chemistry42 by Insilico Medicine — A generative AI platform for small molecule drug discovery, streamlining hit identification to lead optimization.
• ChemML Discovery Platform by Insitro — Accelerating drug discovery through chemistry machine learning and strategic collaborations.
• Cortellis by Clarivate — Equip your R&D ecosystem with intelligence to make confident, speedy decisions across drug and device development.
• Cyprotex by Evotec — Predictive ADME-Tox solutions for enhanced drug safety and success.
• DORA by Insilico Medicine — AI-driven tool to streamline academic paper drafting and accelerate drug discovery research.
• DRG Fusion by Clarivate — Ignite commercial success in life sciences and healthcare with modular analytics powered by real-world data.
• Drug Creation Partnerships by Absci — Leverage Absci's AI and wet lab platform for rapid discovery of breakthrough biologics.
• Drug Discovery & Pre-clinical Development Services by Evotec — Integrated R&D programs and standalone services for accelerated drug discovery and preclinical development.
• EVA Platform by LabGenius — AI-driven platform for ultra-high-throughput protein engineering and antibody discovery.
• EVOcells by Evotec — Integrated iPSC-based platform for cell therapy discovery, development, and manufacturing.
• Evotec AI Platform by Evotec — Accelerating drug discovery and development through integrated AI-driven innovation and advanced technologies.
• Fabry Disease Program (αGal) by Relay Therapeutics — Advancing a preclinical genetic therapy for Fabry Disease.
• GB-0895 by Generate Biomedicines — Long-acting anti-TSLP antibody for severe asthma, advancing to Phase 3.
• GB-7624 by Generate Biomedicines — Anti-IL-13 therapeutic for atopic dermatitis, entering Phase 1 trial in early 2025.
• Geneious Biologics by Dotmatics — Accelerate antibody discovery with cloud-based screening, analysis, and visualization.
• Generate Platform by Generate Biomedicines — Pioneering generative biology to create breakthrough medicines with AI.
• Generative Biologics by Insilico Medicine — AI-powered platform for generative design and optimization of biologics.
• inClinico by Insilico Medicine — Generative AI-powered platform for data-driven, multimodal prediction of clinical trial outcomes.
• Insitro Platform by Insitro — Machine Learning to Unravel Biology and Redefine Disease for Better Drugs, Faster.
• iPSC-based disease modeling by Evotec — Leveraging iPSCs for advanced disease modeling in drug discovery.
• Isomorphic Labs Platform by Isomorphic Labs — Harnessing AI to unlock deeper scientific insights for life-changing medicines.
• Just - Evotec Biologics by Evotec — Accelerating biologics development and manufacturing with J.POD® continuous technology.
• Maestro by Schrödinger — Streamlined portal for predictive computational modeling and machine learning in molecular discovery and materials design.
• Molecular Patient Databases (MPD) by Evotec — Integrates deep human health data to accelerate drug discovery and development.
• Nach01 Foundation Model by Insilico Medicine — AI foundation model for AI-native drug discovery, integrated with Microsoft Discovery and available via AWS Marketplace.
• NeuralPLexer by Iambic Therapeutics — Generative AI for accurate protein-ligand complex structure prediction and small molecule drug design.
• Paige Predict by Tempus AI — AI-powered digital pathology for biomarker prediction from H&E images.
• PandaOmics by Insilico Medicine — AI-powered platform for therapeutic target and biomarker discovery and prioritization.
• PanOmics by Evotec — Comprehensive multi-omics data generation and analysis for advanced drug discovery.
• Pharma.AI by Insilico Medicine — Accelerating drug discovery and development with end-to-end generative AI and automation.
• phenAID by Ardigen — AI-powered image-based virtual screening and phenotypic profiling platform
• PipeBio Antibodies by Benchling — Accelerate AI-powered antibody discovery and development.
• POSH Platform by Insitro — AI-powered platform for gene function reconstruction and accelerated drug discovery.
• PreciousGPT by Insilico Medicine — AI models for digital -omics experiments and drug discovery.
• Recursion OS by Recursion Pharmaceuticals — Decoding biology to radically improve lives through AI-driven drug discovery.
• Relay Platform by Relay Therapeutics — Unlocking protein motion to transform drug discovery for previously intractable targets.
• TargetBench by Insilico Medicine — An AI-empowered benchmarking system for novel drug target identification models.
• TargetPro by Insilico Medicine — AI-empowered platform for rapid and precise novel drug target identification.
• Tempus ECG-Low EF Software by Tempus AI — AI software for early identification of low LVEF from ECGs.
• Tempus ONE by Tempus AI — AI-enabled assistant for rapid insights in precision medicine and research.
• TherML™ Platform by Insitro — End-to-end, modality-agnostic system for designing small molecules, antibodies, oligonucleotides, and biologics.
• Trials by Tempus AI — AI-powered patient identification to accelerate clinical trial enrollment and discovery.
• Valo Opal by Valo Health — AI-powered platform accelerating drug discovery and development with human data.
• XFEP by XtalPi — High-accuracy free energy calculation for predicting protein-ligand binding.
• XGlue™ platform by XtalPi — AI-driven platform for rapid, closed-loop molecular glue drug discovery.
• XMolGen by XtalPi — AI-driven molecular generation for accelerated drug discovery and novel chemical exploration.