SeeSAR
Drug Design Dashboard

| Theme | What this means for a company | How we help |
| Key requirements for biotech startups and larger companies in small-molecule drug discovery | ||
| Science-first, not IT-first | Teams should spend limited resources on core science and pipeline expansion rather than infrastructure, troubleshooting, or learning overhead for complex external tools. | Minimal IT burden; low maintenance; quick onboarding; focus on core competencies |
| Ease of use & integration | Tools must fit into existing workflows and be productive quickly, without requiring dedicated specialists or heavy setup. | Intuitive UI; seamless integration; reliability; stability |
| Speed & efficiency | Companies need short iteration cycles and fast feedback to move from hypotheses to validated next steps. | Fast – Visual – Easy; rapid iteration; quick decisions; accelerated cycles |
| Rapid prototyping & streamlined workflows | Tools should make it easy to test ideas quickly and keep work moving without bottlenecks. | Streamlined workflows; quick access to data; actionable insights; automation |
| Flexibility & scalability | As programs grow, teams need solutions that expand in capability and usage without re-platforming. | Adaptable solutions; scalable workflows; modular expansion; agile approach |
| Cost-effectiveness | Budgeting remains critical: companies need strong value for money, predictable costs, and clear pricing structures. | Value for money; transparent pricing; predictable budgeting; ROI |


| Theme | What this means for a startup | Key aspects |
| Operational and strategic challenges of startups and scaling companies | ||
| Visibility & recognition | Credibility and visibility are vital for attracting funding, partnerships, and top talent. | Recognition; credibility; investor readiness; partner appeal; talent attraction |
| Networking & reputation | Startups benefit from ecosystem access and strong external perception to accelerate collaboration and business development. | Networking; reputation building; community; marketing; promotion |
| Structure & guidance | Many startups lack established processes; they need structure to operationalize discovery efficiently and avoid rework. | Best practices; workflows; SOPs; guidance; operational clarity |
| Mentorship & expertise | Access to experienced experts helps teams set realistic roadmaps, define milestones, and make better decisions early. | Expert support; roadmaps; milestones; enablement; training |

| Theme | Startup reality | BioSolveIT solution |
| Why leaving open-source tools behind matters | ||
| Cost | Open-source tools may seem cheaper initially. | Lower total cost of ownership over time by reducing integration, maintenance, and expertise costs. |
| Adoption | Open-source tools are widely used in biotech startups, especially when resources are tight. | Access to the largest compound collections (combinatorial Chemical Spaces), which is not possible with open-source tools. |
| Workflows | Workflows based on open-source software may already be in use. | Seamless integration into existing workflows, avoiding disruptive transitions. |
| Commitment | Relying on commercial software requires commitment once workflows are established. | Stable, professionally maintained software that ensures long-term reliability. |
| Pricing | Commercial options may seem out-of-league price-wise and are therefore often excluded. | Scalable and versatile solutions that grow with the company as needs evolve. |
| Awareness | Potential users may not be aware of available alternatives that better address project objectives. | Industry-proven algorithms with a strong reputation for reliability and accuracy. |
| Maintenance | Open-source tools require in-house maintenance, troubleshooting, and customization. | Dedicated support infrastructure that ensures quick solutions and continuous operability. |
| Security | Data security is not always a core priority in open-source environments. | Security and confidentiality of proprietary research are built in by design. |
| Training | Training often depends on community resources and trial-and-error. | Tailored training that enables in-house users to operate the software to its full potential. |
| Download page | What it does | Product page | Functionalities | |
| Platforms (GUI) [Info] | ||||
| SeeSAR Drug Design Dashboard |
[SeeSAR] | Interactive structure-based design, docking visualization, SAR analysis, pose inspection, and iterative optimization. Features Chemical Space Docking®. | ||
| infiniSee Chemical Space Navigation Platform |
[infiniSee] | Exploration and visualization of ultra-large Chemical Spaces, similarity search, and data-driven compound prioritization. | ||
| infiniSee xREAL Access Point to Enamine’s Largest Compound Catalog |
[infiniSee xREAL] | Direct access to xREAL, Enamine’s largest make-on-demand compound collection, for virtual screening and custom library generation. | ||
| HPSee Scalable Virtual Screening Workflow Environment |
[HPSee] | High-throughput docking workflows, job management, parallel processing, and large-scale screening campaigns. | ||
| Components (CLI) [Info] | ||||
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FastGrow Pocket Exploration |
[FastGrow] | Growth of fragment hits and (re-)decoration of full-fledged compounds for complementation of a binding site. | |
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FlexX Docking |
[FlexX] | Protein-ligand docking, pose prediction, and binding mode analysis. | |
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FlexS Superposition |
[FlexS] | 3D alignment of ligands, pharmacophore mapping, and shape-based comparison. | |
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HYDE Scoring |
[HYDE] | Hydrogen-bond and desolvation-based scoring for binding affinity estimation. | |
| FTrees Pharmacophore Similarity |
[FTrees] | Fuzzy pharmacophore comparison and similarity search. | ||
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SpaceLight Analog Search |
[SpaceLight] | Fast molecular fingerprint-based analog search across Chemical Spaces. | |
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SpaceMACS Substructure Matching |
[SpaceMACS] | Substructure-based search and SMARTS-supporting pattern matching. | |
| CoLibri Building Spaces |
[CoLibri] | Combinatorial Chemical Space design, reaction enumeration, and virtual space generation. | ||
| Conformator 3D Molecule Ensembling |
[Conformator] | Conformer generation, 3D ensemble building, and geometry optimization. | ||