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Biotechnology PharmaceuticalsTop 10 Best Drug Discovery Software of 2026
Ranking roundup of top drug discovery software tools with criteria and tradeoffs for evaluating labs, including Scilligence, Dotmatics, and MolSoft ICM-Pro.
How we ranked these tools
An editor sets the topic, the inclusion criteria, and the evaluation framework before research starts.
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Framework: Features 40% · Ease 30% · Value 30%
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Scilligence is the best fit for teams that need repeatable hit selection logic with traceable assay-linked compound curation, whereas Dotmatics suits research groups that must govern compound and assay traceability across chem and computation teams with fewer handoffs.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Scilligence
Built-in workflow orchestration links chemical search steps to assay-linked decision artifacts for each target campaign.
Best for: Fits when teams need repeatable hit discovery selection logic with traceable assay-linked compound curation.
Dotmatics
Editor pickStructured object lineage ties compound sets, assay readouts, and analysis outputs into a reviewable history.
Best for: Fits when research groups need governed compound and assay traceability across chem and computation teams.
MolSoft ICM-Pro
Editor pickInteractive protein-ligand editing can feed directly into refinement and scoring without a heavy export-reimport loop.
Best for: Fits when teams run local docking and pose refinement with repeatable scripting and consistent ligand curation.
Comparison Table
Scilligence
vertical specialistScilligence provides chemical registration, inventory, electronic laboratory notebooks, and discovery data management.
Built-in workflow orchestration links chemical search steps to assay-linked decision artifacts for each target campaign.
Scilligence is designed for virtual screening and lead discovery work where compound selection starts with chemical structure search and iterative enrichment. It includes mechanisms for building curated compound collections, running search and filter steps, and exporting selected sets for downstream chemistry and biology activities. The system also records screening-to-assay connections so teams can trace which structures and metadata produced a given shortlist. Administrative controls and governance hooks support shared projects where multiple functions contribute curation and review.
A tradeoff appears in workflow depth versus modeling breadth because Scilligence focuses on organizing discovery, screening preparation, and decision-ready compound handling rather than replacing specialized docking engines. The best fit is when researchers need consistent compound curation and reproducible selection logic across multiple targets, campaigns, and assay sources. A common usage situation involves setting up a repeatable selection workflow for each target, then automating exports and annotations used by lab-ready design and screening teams.
- +Configurable selection workflows that tie search criteria to exported compound sets
- +Strong chemical structure search with consistent similarity and substructure filtering
- +Assay and target context tracking improves traceability across screening campaigns
- +API-oriented integration supports automated handoffs to external tools
- –Modeling and docking coverage is narrower than dedicated in silico toolchains
- –Advanced workflow configuration needs governance discipline to stay reproducible
Medicinal chemistry teams
Shortlist analogs for iterative lead optimization
Faster analog selection cycles
Computational chemistry groups
Automate compound handoffs from models
Less manual data wrangling
Show 2 more scenarios
Translational screening operations
Coordinate multi-target screening data
Improved traceability and review
Operators maintain consistent compound sets and map assay results back to selection criteria.
Data and integration teams
Standardize selection workflows across groups
Higher reproducibility across campaigns
Administrators manage shared configuration so multiple teams execute consistent selection logic and exports.
Best for: Fits when teams need repeatable hit discovery selection logic with traceable assay-linked compound curation.
Dotmatics
enterpriseDotmatics connects scientific data management, laboratory workflows, registration, and discovery analytics.
Structured object lineage ties compound sets, assay readouts, and analysis outputs into a reviewable history.
Dotmatics centers on chemical and assay data objects that connect to downstream analysis, including structure-based screening workflows and lead optimization reporting. The system supports chemical structure search with substructure and similarity modes, and it keeps results tied back to the originating libraries and experiments. Automation is driven through workflow configuration so teams can standardize curation steps, analysis steps, and review views across projects.
A tradeoff is that deeper use of workflow automation and integrations can require a data governance model for identifiers, naming conventions, and data curation ownership. Dotmatics fits teams running design–make–test–analyze cycles where chemists and computational groups need shared traceability from compound records through assay outcomes into prioritization views.
- +Tight linkage between chemical records and assay outcomes for traceable decisions
- +Configurable workflows for repeatable curation and analysis across projects
- +Chemical structure search supports substructure and similarity result workflows
- +Automation and integration focus suit cross-team scientific collaboration
- –Workflow configuration requires strong internal data standards
- –Advanced automation use can take time to align roles and permissions
- –Computational execution depth depends on integration patterns to external engines
- –Dense configuration options increase onboarding effort for new users
Computational medicinal chemists
Virtual screening result triage by structure
Faster hit prioritization
Medicinal chemistry teams
Lead optimization with assay-linked views
More consistent SAR decisions
Show 2 more scenarios
Data and informatics managers
Assay data management at scale
Lower curation overhead
Standardize import, normalization, and review workflows for assay records and their linked compounds.
Program leadership
Portfolio governance across projects
Reduced cross-project ambiguity
Enforce controlled access and review processes around scientific objects used for design and reporting.
Best for: Fits when research groups need governed compound and assay traceability across chem and computation teams.
MolSoft ICM-Pro
vertical specialistICM-Pro provides protein modeling, docking, virtual screening, molecular dynamics, and structure analysis.
Interactive protein-ligand editing can feed directly into refinement and scoring without a heavy export-reimport loop.
MolSoft ICM-Pro groups ligand preparation, structure alignment, docking, and result analysis around a shared internal model, which reduces format switching during iterative lead optimization cycles. Interactive 3D modeling and pose inspection connect directly to batch docking and scoring workflows, which helps teams move from hypotheses to ranked poses without exporting to multiple tools. It supports chemical structure search behaviors like similarity and substructure targeting to drive candidate triage before modeling runs.
A key tradeoff is that API and automation depth is less aligned to modern LIMS and cloud orchestration patterns than toolchains built around broader enterprise integration layers. It fits best when a team runs repeated structure modeling jobs on local compute and needs consistent curation of ligands, proteins, and docking settings across many iterations. For teams that require governance features like RBAC and audit logs for cross-team sharing, additional systems and operational controls may be needed outside the desktop workflow.
- +Pose refinement and interactive inspection stay connected to docking outputs
- +Stereochemistry-aware ligand handling reduces manual correction steps
- +Batch workflows reuse consistent modeling settings across targets
- +Chemical search tools support triage before computational modeling runs
- –Automation and integration controls feel lighter than enterprise lab platforms
- –Desktop-first workflow can slow cross-team collaboration at scale
- –Advanced configuration requires domain familiarity to avoid silent modeling drift
- –File interoperability can add overhead when mixing multiple vendor pipelines
Computational chemistry teams
Iterative lead optimization via pose refinement
Shorter cycles to ranked poses
Medicinal chemistry groups
Structure-based triage from hit libraries
More focused design-make-test
Show 1 more scenario
Structural bioinformatics analysts
Protein-ligand structure comparison
Clearer SAR hypotheses
Align binding modes and inspect key interactions across multiple ligands and conformations.
Best for: Fits when teams run local docking and pose refinement with repeatable scripting and consistent ligand curation.
Schrödinger
enterpriseIntegrated molecular modeling software supports structure-based drug design, virtual screening, and molecular dynamics.
Integrated scientific workflow orchestration that chains docking, modeling, and property evaluation into repeatable run sequences.
Schrödinger is a drug discovery software suite that links computational chemistry engines with workflow execution for small-molecule design and optimization. The core capabilities cover molecular docking, pharmacophore modeling, and property-driven lead optimization with structure-based and ligand-based screening workflows.
- +Docking and pharmacophore workflows match structure-based screening use cases
- +Workflow orchestration supports multi-step design to analysis pipelines
- +Compute-first tooling fits teams running iterative modeling cycles
- +Scriptable runs support repeatability across hit-to-lead experiments
- –Governance and admin controls are weaker than dedicated LIMS-style systems
- –Requires workflow discipline to keep parameterization consistent across runs
- –Collaboration features lag behind assay-centric data platforms
- –Data integration depends on how discovery structures and outputs are exported
Best for: Fits when computational chemistry teams need coordinated docking, pharmacophore modeling, and iterative optimization workflows.
BIOVIA Discovery Studio
enterpriseDiscovery Studio provides molecular modeling, simulation, structure-based design, and biological analysis tools.
Protein-ligand interaction visualization tied directly to docking and analysis outputs for rapid hypothesis checking.
BIOVIA Discovery Studio runs structure- and ligand-informed workflows that combine receptor interaction analysis, molecular docking, and visualization in one environment. It also supports pharmacophore modeling, property and toxicity prediction, and cheminformatics searches across chemical structures and assay-linked datasets.
- +End-to-end design-to-visual inspection for docking and protein-ligand interactions
- +Extensive built-in cheminformatics operations for structure, substructure, and similarity search
- +Protocol-driven workflow runs for repeated virtual screening and analysis batches
- +Scientific workflow integration for modeling outputs feeding follow-on analyses
- –Workflow configuration can become complex across multiple modules
- –Advanced automation depends on external integration patterns rather than a single managed API
- –Large dataset performance may require careful staging and index planning
- –Governance and audit visibility are weaker than full LIMS-style platforms
Best for: Fits when chemistry teams need interactive modeling, docking, and SAR analysis inside one workflow.
Aqemia
AI specialistAqemia develops physics-based generative modeling software for small-molecule discovery.
Workflow automation that preserves assay-to-compound linkage across project steps and minimizes manual re-mapping.
Aqemia is drug discovery software for teams that need chemistry and biology workflows coordinated inside a single operational environment. Its core capabilities focus on compound-centric data handling, target and assay context management, and workflow automation that connects screening outputs to downstream analysis. Aqemia also supports integration needs through an API-oriented approach for programmatic data movement and system-to-system workflows.
- +Workflow automation ties assay data context to downstream analysis steps
- +Compound-first organization supports consistent reuse across project phases
- +API-oriented integration supports programmatic data movement and orchestration
- +Configurable setups help standardize collaboration across teams
- –Setup and configuration require governance discipline to keep projects consistent
- –Deep cheminformatics coverage can be limited without external tooling
- –Advanced modeling workflows may need stronger native analytics packaging
- –Reporting and export controls can feel constrained for highly customized pipelines
Best for: Fits when drug discovery groups need managed workflows linking compound records to assay context and automated handoffs.
Benchling
enterpriseBenchling manages biological data, experimental workflows, inventory, and research collaboration in a cloud platform.
Granular experiment and sample provenance in the ELN model with configurable audit-friendly state transitions.
Benchling is built around a curated electronic lab notebook workflow that links sample, protocols, and results into a traceable chain of custody. It supports compound library management and assay data capture with configurable forms and structured fields for downstream analysis.
- +Tight ELN-to-result traceability for samples and experiments
- +Configurable assay templates reduce manual data entry drift
- +Search and retrieval across compounds, files, and experiments
- +Extensibility via API for workflow integration and data sync
- –Advanced cheminformatics and modeling depth is not its core focus
- –Workflow configuration can become complex across multiple teams
- –Data migration to new schemas needs careful planning
- –Less coverage for structure visualization tooling than modeling-first suites
Best for: Fits when drug discovery teams need governed ELN workflows that connect sample tracking, assay results, and external systems.
NVIDIA BioNeMo
API-firstBioNeMo provides cloud and software tools for generative artificial intelligence in molecular and biological research.
Model and workflow execution built to run training and inference on NVIDIA GPU infrastructure with batched scientific pipelines.
NVIDIA BioNeMo is positioned for end-to-end scientific computing in drug discovery by focusing on model training and inference workflows that run on NVIDIA GPU infrastructure. Core capabilities include workflow orchestration for cheminformatics and bioinformatics tasks, plus support for deep learning model development pipelines that can incorporate protein, structure, and compound representations.
- +GPU-first workflow design for high-throughput model training and inference runs
- +Workflow orchestration built for repeatable dataset preprocessing and batched scoring
- +Extensible deep learning pipelines for custom protein and compound representations
- +Strong fit for teams standardizing compute stacks across projects
- –Less comprehensive than dedicated drug discovery informatics tools for assay-centric data modeling
- –Workflow governance features like RBAC and audit logs are not the primary focus
- –Integration effort is higher when starting from non-NVIDIA ML and docking pipelines
- –Breadth for routine medicinal chemistry steps can lag specialized discovery suites
Best for: Fits when teams need GPU-accelerated model workflows and automation across training, embedding, and scoring steps.
OpenEye Orion
cloud platformOrion is a cloud platform for scalable molecular design, cheminformatics, screening, and computational workflows.
Orion applies OpenEye engine logic for chemical search and structure filtering inside repeatable, project-scoped workflows.
OpenEye Orion connects OpenEye cheminformatics engines to project workflows for tasks like chemical structure search, similarity and substructure matching, and filtering large compound sets. It also supports scientific file handling for common molecular formats and integrates with upstream and downstream discovery tooling to keep molecules, annotations, and results aligned across steps.
- +Direct integration with OpenEye search and filtering engines for chemical structure matching
- +Consistent handling of common molecular file formats and structure representations
- +Repeatable workflow runs to standardize processing across compound library updates
- +Project-scoped data organization to keep results tied to specific discovery contexts
- –Requires cheminformatics workflow configuration to achieve production-grade automation
- –Not designed for assay-heavy data management compared with ELN-centric drug discovery systems
Best for: Fits when cheminformatics-heavy teams need automated structure search workflows tied to managed discovery projects.
Certara D360
enterpriseD360 organizes research data, scientific workflows, and analytical outputs for pharmaceutical development teams.
Assay-linked, structure-aware record keeping that keeps chemistry and experimental results aligned across configurable workflows.
Certara D360 is a drug discovery data and workflow environment focused on managing chemical and biological information tied to project execution. It combines structure-aware search and assay-linked record keeping with configurable work processes that track experiments from design inputs through outcomes.
- +Project-centric experiment records link assays to chemical structures for traceability
- +Configurable workflows support repeatable design–make–test–analyze tracking
- +Structure-aware search improves retrieval for structure series and related entries
- +Integration and automation support reduce manual data re-entry across teams
- –Greater setup effort than generic ELN systems for workflow configuration and governance
- –Depth in modeling and docking workflows is less complete than dedicated in silico tools
Best for: Fits when mid-size teams need structured experiment tracking tied to chemistry and assay outcomes.
Conclusion
After evaluating 10 drug discovery software, Scilligence stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Drug Discovery Software
Drug discovery software is used to connect chemical records, assay context, and computational outputs into governed workflows that support hit discovery and lead optimization. This guide covers Scilligence, Dotmatics, MolSoft ICM-Pro, Schrödinger, BIOVIA Discovery Studio, Aqemia, Benchling, NVIDIA BioNeMo, OpenEye Orion, and Certara D360 based on the workflow depth, traceability, and automation behavior shown in the tool cards.
The selection criteria emphasize integration depth and how each platform keeps decisions reproducible across teams. Scilligence is highlighted for workflow orchestration that links chemical search steps to assay-linked decision artifacts for each target campaign. Dotmatics and Benchling are highlighted for governed lineage and ELN-style provenance, while Schrödinger and BIOVIA Discovery Studio are highlighted for computational workflow chaining that stays connected to docking and analysis outputs.
Drug Discovery Software for governed hit discovery, docking workflows, and assay-linked compound selection
Drug discovery software is designed to manage the end-to-end chain from compound curation and structure search to assay-linked decision artifacts and iterative optimization. Scilligence ties search criteria to exported compound sets through configurable selection workflows that preserve traceability from assay-linked context to curated outputs.
Dotmatics uses structured object lineage to connect compound sets, assay readouts, and analysis outputs into a reviewable history for cross-team governance. Across these systems, automation depth is measured by whether workflow configuration keeps parameterization consistent and whether the platform supports repeatable run sequences without requiring heavy manual re-mapping between chemistry records and computational steps.
Integration and traceability controls for drug discovery workflows
Drug discovery software must connect chemical records, assay context, and computational outputs into governed sequences without breaking traceability between steps. Scilligence leads on workflow orchestration that links chemical search steps to assay-linked decision artifacts per target campaign, which determines whether curated outputs stay explainable months later.
Assay-linked workflow orchestration and traceable decision artifacts
Scilligence connects chemical search criteria to exported compound sets through configurable selection workflows tied to assay-linked decision artifacts. Aqemia preserves assay-to-compound linkage across project steps with workflow automation that reduces manual re-mapping.
Object lineage for governed compound and assay history
Dotmatics stores structured object lineage that ties compound sets, assay readouts, and analysis outputs into a reviewable history. Benchling provides granular experiment and sample provenance in its ELN model with configurable audit-friendly state transitions.
End-to-end computational chaining from docking to property and analysis outputs
Schrödinger chains docking, pharmacophore modeling, and property evaluation into repeatable run sequences. BIOVIA Discovery Studio connects docking and protein-ligand interaction visualization directly to analysis outputs for rapid hypothesis checking.
Protein-ligand modeling that stays connected to pose editing and refinement
MolSoft ICM-Pro supports interactive protein-ligand editing feeding directly into refinement and scoring without a heavy export-reimport loop. BIOVIA Discovery Studio emphasizes protein-ligand interaction visualization tied to docking and analysis for in-workflow interpretation.
Workflow automation that scales across compute-heavy training and inference runs
NVIDIA BioNeMo focuses on GPU-first model and workflow execution built for training and inference on NVIDIA infrastructure with batched scientific pipelines. Schrödinger provides multi-step orchestration for computational chemistry sequences that keep coordinated docking and modeling iterations tied together.
Match workflow governance and automation surface to the team’s discovery pattern
The deciding factor is not whether a platform can run search or docking, it is whether workflow configuration keeps parameterization consistent and preserves the linkage between assay context and curated chemistry outputs. A second factor is how each system behaves when cross-team work requires repeatability, because several platforms demand governance discipline to keep automation reproducible.
Pick the system that will hold the assay-to-compound linkage through curation
Choose Scilligence when chemical search steps must produce assay-linked selection artifacts per target campaign with repeatable exported compound sets. Choose Aqemia when workflows must automatically preserve assay context to downstream analysis handoffs with compound-first organization.
Choose lineage depth if multiple teams need reviewable provenance
Choose Dotmatics when compound sets, assay readouts, and analysis outputs must be connected through structured object lineage that supports traceable decisions across chem and computation teams. Choose Benchling when governed ELN workflows must connect samples and experiments to assay results with configurable audit-friendly state transitions.
Select compute chaining depth for docking and iterative optimization workflows
Choose Schrödinger when docking, pharmacophore modeling, and property evaluation must run as coordinated multi-step sequences with a single workflow orchestration layer. Choose BIOVIA Discovery Studio when interactive protein-ligand interaction visualization must remain tied to docking and SAR analysis inside the same modeling workflow.
Choose pose refinement ergonomics for teams that edit ligands in the modeling loop
Choose MolSoft ICM-Pro when interactive protein-ligand editing must feed directly into refinement and scoring while stereochemistry-aware ligand handling reduces manual correction. Choose BIOVIA Discovery Studio when interpretation depends on protein-ligand interaction visualization tied directly to docking and analysis outputs.
Choose the platform architecture that matches the scale of model training and batched inference
Choose NVIDIA BioNeMo when GPU-accelerated training and inference must run as batched scientific pipelines with dataset preprocessing and scoring steps orchestrated for throughput. Choose Schrödinger when the priority is coordinated computational chemistry workflows that chain docking and modeling into iterative optimization runs with repeatable parameterization.
Teams that should prioritize these platforms for Drug Discovery Software workflows
Drug discovery software selection should track whether the organization needs assay-linked traceability, governed ELN provenance, or compute orchestration that keeps modeling parameters consistent. The strongest fit is typically determined by how often the team changes workflow configuration and how many stakeholders need to audit decisions end to end.
Hit discovery teams running repeatable target campaign selection logic
Scilligence fits when chemical search criteria must produce assay-linked selection outputs with traceable compound curation per target campaign.
Cross-team chemistry and computation groups that need reviewable provenance
Dotmatics fits when object lineage must connect compound sets, assay readouts, and analysis outputs into a history chem and computation teams can review. Benchling fits when sample tracking and experiment states must be governed inside ELN workflows that connect to assay results.
Computational chemistry teams chaining docking to pharmacophore and property evaluation
Schrödinger fits when docking and pharmacophore workflows must run as orchestrated multi-step sequences into repeatable run sequences.
Teams that depend on interactive pose editing and refinement loops
MolSoft ICM-Pro fits when protein-ligand editing must stay connected to docking outputs for pose refinement and scoring with consistent ligand curation.
ML-driven teams running GPU training and inference pipelines for scoring
NVIDIA BioNeMo fits when workflow execution must run training and inference on NVIDIA GPU infrastructure with batched dataset preprocessing and scoring automation.
Where drug discovery teams go wrong with workflow governance and automation
The most common failures happen when workflow configuration becomes inconsistent across projects or when assay linkage is lost during export and re-import between systems. Another frequent issue is expecting enterprise governance controls inside platforms that focus on specialized modeling or GPU inference rather than assay-centric data modeling.
Using a chemical search tool without enforcing assay-linked outputs for each target campaign
Scilligence is built to tie search criteria to exported compound sets through assay-linked selection workflows. Aqemia also targets assay-to-compound linkage preservation to avoid manual re-mapping during handoffs.
Assuming lineage depth will be automatic without enforcing internal data standards
Dotmatics can deliver reviewable compound and assay history through structured object lineage, but workflow configuration requires strong internal data standards to stay consistent. Benchling can reduce manual drift with configurable assay templates, but workflow configuration can still become complex across multiple teams.
Chaining docking and modeling steps without a single orchestration layer
Schrödinger chains docking, pharmacophore modeling, and property evaluation into repeatable run sequences. BIOVIA Discovery Studio keeps visualization and interpretation tied to docking and analysis outputs, which reduces the risk of losing context after each step.
Over-relying on interactive pose editing while ignoring cross-team integration and governance controls
MolSoft ICM-Pro keeps pose refinement connected to docking outputs with stereochemistry-aware ligand handling. Desktop-first workflow design can slow cross-team collaboration at scale, so teams need a governance plan for shared outputs.
Treating GPU training platforms as substitutes for assay-centric data modeling
NVIDIA BioNeMo is optimized for GPU-first workflow execution and batched scientific pipelines. Governance features like RBAC and audit logs are not its primary focus, so assay-centric traceability may require pairing with other drug discovery informatics layers.
How We Selected and Ranked These Tools
We evaluated Scilligence, Dotmatics, MolSoft ICM-Pro, Schrödinger, BIOVIA Discovery Studio, Aqemia, Benchling, NVIDIA BioNeMo, OpenEye Orion, and Certara D360 by focusing on integration depth, traceability behavior, and automation and workflow orchestration consistency. Features accounted for 40% of the score because repeatable docking, modeling, and assay-linked compound selection depends on how workflows preserve linkage between chemical records and decision artifacts.
Ease and value each accounted for 30% because workflow configuration discipline and cross-team usability determine whether automated sequences stay reproducible in practice. Scilligence ranked highest because its built-in workflow orchestration links chemical search steps to assay-linked decision artifacts for each target campaign with consistent curation outputs.
Frequently Asked Questions About Drug Discovery Software
How do Scilligence and Dotmatics differ in hit discovery workflow orchestration and decision traceability?
Which tool is better for structure-only matching at scale: OpenEye Orion or Schrödinger?
How does assay-to-compound linkage survive handoffs in Aqemia versus Benchling?
What integrations and APIs matter most when automation must connect discovery steps to external systems?
Which platform best supports admin controls for access governance in cheminformatics workflows: Orion or Benchling?
When teams need model training and batched scoring on GPU infrastructure, how does BioNeMo fit compared with MolSoft ICM-Pro?
What breaks if workflow orchestration is missing when chaining docking, visualization, and analysis steps?
Which tool is most suited to interactive protein-ligand editing that feeds directly into refinement and scoring?
How do Scilligence and Certara D360 handle structure-aware record keeping across the design–make–test–analyze cycle?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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