OPTIC

Name: OPTIC
Brand: OPTIC

AI Native

AI-powered drug discovery platform for rapid molecule screening and testing, from target identification to lab validatio

Est 2022|Raised $11M

LinkedIn 4K|Web traffic 1K|FTEs 268

01Competitive Landscape

Narrative competitive analysis

- BIOPTIC vs Schrödinger: Schrödinger typically offers a fuller platform (physics-based methods, informatics, enterprise governance). BIOPTIC differentiates with claims of large-scale library retrieval and packaged hit lists focused on hit finding. - BIOPTIC vs Atomwise: Atomwise emphasizes deep learning for structure-based discovery and has an established partnership portfolio. BIOPTIC differentiates with a search-engine approach to large library retrieval and with ligand- and sequence-based models. - BIOPTIC vs Insilico / Recursion: Insilico and Recursion position as platform-plus-pipeline organizations (and in Recursion’s case, industrial wet labs and large proprietary datasets). BIOPTIC is positioned more as a service/platform delivered to partners plus agentic scouting.

When customers choose competitors instead (likely reasons)

Based on typical buyer behavior in this category:

need for SOC 2 / ISO evidence and mature enterprise controls → choose vendors with established compliance programs
preference for integrated in silico + wet lab loop under one roof → choose Recursion-like models
need for broad, validated modeling coverage beyond hit finding (e.g., physics-based methods) → choose Schrödinger

	OPTIC / BIOPTIC	Schrödinger (LiveDesign)	Atomwise (AtomNet)
Starting Price	Published project-based pricing available from the vendor; multiple-iteration packaged offerings noted by the vendor	Enterprise-tier pricing; not publicly standardized	Partnership/collaboration-based pricing; details available from the vendor
Best suited for	Teams seeking rapid hit lists from very large vendor libraries and packaged deliverables	Organizations needing end-to-end computational chemistry, physics-based methods, and collaborative informatics	Organizations seeking structure-based hit discovery via deep learning and partnership models
Key Strength	Published evidence of large-scale retrieval and stated deliverables (order-ready lists)	Mature ecosystem, enterprise workflows, broad method suite	Established presence in AI SBDD with multiple partnerships
Key Weakness	Limited public security/compliance detail; limited third-party user reviews	Cost and complexity; learning curve	Typically collaboration-based rather than productized self-serve options

01Competitive Landscape

Narrative competitive analysis

BIOPTIC vs Schrödinger: Schrödinger typically offers a fuller platform (physics-based methods, informatics, enterprise governance). BIOPTIC differentiates with claims of large-scale library retrieval and packaged hit lists focused on hit finding.
BIOPTIC vs Atomwise: Atomwise emphasizes deep learning for structure-based discovery and has an established partnership portfolio. BIOPTIC differentiates with a search-engine approach to large library retrieval and with ligand- and sequence-based models.
BIOPTIC vs Insilico / Recursion: Insilico and Recursion position as platform-plus-pipeline organizations (and in Recursion’s case, industrial wet labs and large proprietary datasets). BIOPTIC is positioned more as a service/platform delivered to partners plus agentic scouting.

When customers choose competitors instead (likely reasons)

Based on typical buyer behavior in this category:

need for SOC 2 / ISO evidence and mature enterprise controls → choose vendors with established compliance programs
preference for integrated in silico + wet lab loop under one roof → choose Recursion-like models
need for broad, validated modeling coverage beyond hit finding (e.g., physics-based methods) → choose Schrödinger

	OPTIC / BIOPTIC	Schrödinger (LiveDesign)	Atomwise (AtomNet)
Starting Price	Published project-based pricing available from the vendor; multiple-iteration packaged offerings noted by the vendor	Enterprise-tier pricing; not publicly standardized	Partnership/collaboration-based pricing; details available from the vendor
Best suited for	Teams seeking rapid hit lists from very large vendor libraries and packaged deliverables	Organizations needing end-to-end computational chemistry, physics-based methods, and collaborative informatics	Organizations seeking structure-based hit discovery via deep learning and partnership models
Key Strength	Published evidence of large-scale retrieval and stated deliverables (order-ready lists)	Mature ecosystem, enterprise workflows, broad method suite	Established presence in AI SBDD with multiple partnerships
Key Weakness	Limited public security/compliance detail; limited third-party user reviews	Cost and complexity; learning curve	Typically collaboration-based rather than productized self-serve options

OPTIC

01Competitive Landscape

Narrative competitive analysis

When customers choose competitors instead (likely reasons)

02Market Map

01Competitive Landscape

Narrative competitive analysis

When customers choose competitors instead (likely reasons)

02Market Map