For: R&D, computational chemistry, informatics, procurement, and finance leaders
Goal: Compare GenAI drug-discovery proposals beyond licence price
Focus: Commercial model, total cost of ownership, data and IP rights, scientific evidence, security, and validation
Normalise vendor proposals into a comparable 12–36- month cost view
Identify hidden costs in compute, data onboarding, integration, and scientific services
Protect data, model, and molecule ownership before contracting
Define a milestone-gated proof of concept before scaling
Evaluating a generative AI drug-discovery platform can differ materially from buying conventional enterprise software. Depending on the vendor and use case, an agreement may combine platform access with proprietary-data onboarding, compute consumption, bespoke model adaptation, scientific services, experimental validation, or success-based collaboration economics.
Two proposals with similar annual licence fees can therefore have very different three-year costs, delivery obligations, data-rights implications, and exposure to milestones or royalties. A robust comparison should assess the full cost and risk of the intended discovery workflow, not just the subscription price.
This checklist provides a consistent framework for comparing GenAI and generative-chemistry proposals across commercial, scientific, technical, and contractual terms.
Share the platform, intended workflow, and commercial model you are considering. We'll help you identify the questions to ask about total cost, data and IP rights, scientific evidence, validation, and implementation risk.
Before comparing line items, establish the vendor’s primary commercial model. Many suppliers combine elements of more than one model, so request a written proposal that separates platform fees, usage charges, services, and contingent economics.
Public AI-drug-discovery collaborations demonstrate that upfront payments, milestone payments, and royalties can be part of discovery-partner economics, but their scale and structure depend on the target, rights granted, asset stage, and scope of work. They should never be inferred from a vendor’s headline platform price.
A meaningful total-cost-of-ownership comparison should cover the expected contract period—commonly 12 to 36 months—and use the same intended use case for every vendor. Ask suppliers to itemize fixed, variable, optional, and contingent costs separately.
Confirm the base subscription fee and exactly what it includes:
Platform modules, model families, workflow capabilities, API access, and reporting.
Named users, concurrent users, projects, active targets, programmes, compounds, or tenant environments.
Training, implementation, customer-success support, technical support, service-level commitments, and upgrades.
Geographic, affiliate, subsidiary, or external-CRO access rights.
Renewal caps, price escalators, minimum term, auto-renewal conditions, and termination fees.
Ask: What functional, volume, user, or programme limits apply before additional charges are triggered?
Data preparation can be as important as the platform licence. Identify all activities required to make internal data usable, including assay-data cleanup, normalization, ontology mapping, compound-structure reconciliation, access-control setup, retrieval configuration, fine-tuning, and bespoke model development.
Ask: Which data-onboarding, engineering, model-adaptation, and validation deliverables are included in the base agreement, and which require a separate statement of work?
Require a written scope that states assumptions, delivery dates, customer dependencies, acceptance criteria, change-control rates, and ownership of data-processing artefacts.
GenAI and computational-chemistry workflows may include model inference, virtual screening, structural modeling, molecular dynamics, storage, API calls, or third-party infrastructure. The operational unit may be GPUs, credits, model runs, compounds screened, API calls, storage, or another vendor-defined measure.
Ask: What compute, storage, API, and model-run allowance is included; how are overages calculated; and what monitoring or spending controls can the customer use?
Request a usage estimate based on your planned target count, library size, number of iterations, user population, and expected experimental feedback cycles. Confirm whether unused allowances roll over, expire, or are transferable between programmes.
Budget for integration with existing systems such as ELNs, LIMS, compound registries, data lakes, identity-management tools, laboratory automation, and analytics environments. Establish whether APIs, connectors, SSO, data export, custom workflow configuration, and dedicated technical support are included or separately charged.
Ask: Who is responsible for integration, model updates, data refreshes, version compatibility, support, and incident response after go-live?
Do not assume that ongoing model monitoring, integration maintenance, platform upgrades, or bespoke scientific support are included in a licence fee.
An AI-generated output is a scientific hypothesis until it is assessed through suitable experimental work. Determine whether the vendor provides internal wet-lab capabilities, coordinates CRO work, offers preferred-provider arrangements, or delivers only software outputs.
Ask: Does the agreement include any compound synthesis, assay development, screening, medicinal-chemistry review, or experimental validation—and who pays, controls, owns, and reviews those outputs?
For a collaboration model, separately document research funding, success milestones, royalties, options, exclusivity, territory, field-of-use restrictions, intellectual-property ownership, and rights after termination. Public collaborations may include substantial contingent payments, but deal structures are negotiated individually.
A GenAI platform can process some of a biotech’s most valuable assets: target hypotheses, structures, assay results, negative data, programme strategy, and invention disclosures. Do not rely on informal assurances; place the terms in the contract and applicable data-processing documentation.
Confirm in writing:
Ownership and permitted use of input data, generated structures, scores, predictions, assay feedback, reports, and workflow outputs.
Whether the vendor, affiliates, subcontractors, or underlying model providers may access, retain, aggregate, de-identify, or use customer data.
Whether customer information may be used to train, evaluate, fine-tune, benchmark, or improve general or customer-specific models.
Rights to model artefacts, tuned models, prompts, retrieval indexes, custom workflows, and derivative outputs.
Patent-prosecution support, invention disclosure procedures, publication review, confidentiality, and rights to continue developing outputs after contract termination.
Data export format, timing, cost, migration support, retention period, and deletion-certification process.
Ask: Which party owns each input, output, invention, model improvement, and derivative work—and what rights survive termination?
Assess the architecture against the sensitivity of your programme data:
Deployment model: shared SaaS, isolated tenant, private cloud/VPC, customer cloud, or on-premises deployment.
Data residency, cross-border data transfers, subprocessors, and locations where model inference or support access occurs.
Encryption in transit and at rest, identity and access management, SSO, role-based access, audit logs, backup, recovery, and incident notification.
Security evidence available for review, such as independent assurance reports, ISO certifications, penetration-test summaries, vulnerability management, and security questionnaires.
Procedures for model updates, change management, release notes, rollback, and logging of material workflow changes.
Ask: Can you provide an auditable description of where our data is processed, who can access it, whether it is isolated, and whether it is ever used to improve shared models?
Platform selection should be based on evidence for the relevant biological target, modality, chemical space, and decision point—not solely on demonstration outputs, publication counts, or general benchmark claims.
Ask vendors to describe:
The intended use and scientific applicability domain of each relevant model.
Training-data provenance, data-quality controls, known gaps, bias risks, and restrictions on use.
Retrospective, benchmark, and prospective evidence relevant to your target class, modality, and assay context.
How uncertainty, confidence, out-of-domain inputs, contradictory evidence, and failed predictions are identified and communicated.
The version of each model, source dataset, parameters, workflow configuration, and code or API version used for a specific run.
How model updates are governed, communicated, validated, and compared with previous versions.
The availability of reproducible logs, exportable results, ranking rationale, and source-data provenance.
A credible evaluation distinguishes reproducibility, retrospective benchmark performance, and prospective experimental validation. Retrospective success can establish workflow fit, but it is not equivalent to proving that a platform will generate novel, experimentally successful candidates for a new programme.
Do not enter a multi-year agreement without a scoped, time-boxed proof of concept where practical. Before the vendor runs the platform, document the target, permitted data, baseline approach, output rights, candidate-selection rules, assay plan, timeline, and acceptance criteria.
Avoid generic productivity metrics such as “molecules generated per hour” unless they relate directly to your decision. Define what success means for the programme: for example, improvement over an existing screen, quality of candidate prioritization, time to a testable set, experimental hit rate, selectivity, ADMET profile, novelty, or the proportion of candidates considered feasible by medicinal chemists.
Prospective experimental assessment is especially important because there is no universally accepted, real-world validation standard for molecular-generative models, and benchmark results can fail to predict practical discovery outcomes.
Base subscription, implementation, support, and optional-service fees are separately itemized.
Limits for modules, users, programmes, targets, compute, APIs, storage, and data volume are documented.
Overage rates, spend controls, rollover rules, price escalators, renewal terms, and termination charges are clear.
Pilot or POC fees, conversion terms, and rights to POC outputs are documented.
Research funding, milestones, royalties, options, exclusivity, and field-of-use restrictions are fully itemized where relevant.
Ownership and permitted use of inputs, outputs, candidates, predictions, and assay feedback are explicit.
Any vendor right to use customer data for model training, benchmarking, or improvement is expressly accepted, limited, or prohibited.
The buyer can export data, results, audit logs, and workflow artefacts in usable formats.
Post-termination access, migration assistance, retention, deletion, and deletion certification are agreed.
Confidentiality, publication review, invention disclosure, patent-support, and derivative-rights processes are documented.
Deployment model, data residency, subprocessors, and support-access locations meet organizational requirements.
Authentication, authorization, encryption, audit logging, backup, recovery, and incident-notification controls are documented.
Compute, API, storage, and inference allowances are stated, with overage calculation and budget controls.
Integration responsibilities for ELNs, LIMS, compound registries, APIs, SSO, and data platforms are assigned.
Model and platform update processes, release documentation, change control, and rollback procedures are defined.
Intended use, applicability domain, evidence base, and known limitations are documented for each proposed workflow.
The vendor can provide reproducible records of inputs, model versions, settings, outputs, and ranking logic.
The POC uses pre-agreed, locked success criteria and a relevant internal baseline.
Prospective candidates are assessed for novelty, synthesizability, and a credible experimental path.
Both positive and negative experimental findings are captured and reviewed before a scale-up decision.
The final recommendation weighs scientific value, operational fit, legal terms, and full programme cost—not the headline licence fee alone.
A pricing and procurement framework becomes more useful when applied to a real shortlist. Explore HealthyData.Science’s in-depth buyer decision profiles to assess individual AI drug-discovery platforms through their technical approach, workflow fit, commercial-model signals, publicly available evidence, implementation considerations, buyer FAQs, alternative tools, and comparison pages.
The three profiles below illustrate different AI drug-discovery approaches. They are starting points for research, not endorsements or substitutes for scientific, security, legal, or procurement due diligence.
Structure-based virtual screening for small-molecule discovery
Atomwise’s AtomNet platform applies deep learning to structure-based virtual screening and compound prioritisation for early small-molecule discovery. Review its buyer decision profile to assess the evidence, virtual-screening workflow, discovery-collaboration model, data and IP questions, and alternatives relevant to hit identification and lead optimisation.
Explore the Atomwise buyer decision profile
Integrated AI platform for targets, molecules, and drug development
Insilico Medicine’s Pharma.AI platform combines target discovery, generative chemistry, and clinical-development prediction in a broader AI-enabled drug-discovery stack. Its profile is useful for buyers comparing integrated platform access, bespoke discovery support, automated-lab capabilities, collaboration economics, and the evidence needed for an end-to-end discovery evaluation.
Explore the Insilico Medicine buyer decision profile
Knowledge-graph-led target discovery and hypothesis generation
BenevolentAI applies machine learning and a biomedical knowledge-graph approach to connect scientific literature, clinical-trial data, real-world data, and other biomedical evidence for target identification and therapeutic hypothesis generation. Its profile supports early-stage evaluation of evidence provenance, target-validation workflows, data integration, strategic collaborations, and alternative target-discovery platforms.
Explore the BenevolentAI buyer decision profile
Need a broader shortlist?
Explore more AI drug discovery platforms by workflow, including target discovery, generative chemistry, virtual screening, lead optimisation, and experimental validation.
EDITORIAL DISCLOSURE: HealthyData’s in-depth buyer decision profiles synthesise publicly available information and editorial analysis to support initial vendor research. Some enhanced profiles may be sponsored or featured; sponsorship may affect visibility or profile features, but does not determine HealthyData’s factual analysis, risk assessment, or comparative conclusions.
Shortlisting a generative-chemistry, AI target-discovery, or drug-development platform? Browse tools by workflow, deployment approach, commercial model, evidence signals, and implementation considerations in the HealthyData.Science Vendor Directory.
