Entrepreneurship

The development of protein-based therapeutics traditionally follows a multi-stage pipeline encompassing target selection, protein design, laboratory screening, optimization, and validation.

1. Target Selection – Researchers begin by identifying disease-related molecular targets (such as enzymes, receptors, or signaling proteins). This process heavily depends on prior biological knowledge and experimental screening, often requiring months of in vitro and in vivo studies to ensure relevance and specificity.
2. Protein Design (Mutagenesis / Rational Design) – Candidate proteins are engineered using site-directed mutagenesis or rational design based on structural data. These methods rely on manual iteration and prior domain expertise, making them labor-intensive and limited by human hypothesis bias.
3. Lab Screening (High-Throughput Screening, HTS) – Thousands of designed variants undergo HTS to evaluate binding affinity, solubility, and stability. However, the screening process is time-consuming and costly, requiring robotic automation and large reagent volumes.
4. Optimization (Cycles of Redesign) – Promising hits are refined through multiple redesign and testing cycles to improve their biochemical and pharmacokinetic profiles. This stage can take several months to years, leading to extended development timelines and high R&D costs.
5. Validation – Final candidates undergo validation assays and preclinical testing. Despite extensive effort, many fail due to poor folding, low activity, or unfavorable immunogenicity, resulting in a low overall success rate (<10%) from discovery to preclinical transition.

In summary, the traditional protein design workflow is fragmented, slow, and resource-intensive—characterized by high costs, lengthy iteration cycles, and low predictability. This inefficiency underscores the urgent need for a next-generation platform that integrates AI-driven learning with automated experimental evolution, enabling faster and more cost-effective protein therapeutic discovery—precisely the problem Protovate aims to solve.

A scientist signs in and uploads a starting sequence or structure. In the same screen, they can add optional conditioning—target organism, secretion tag preferences, pH/temperature window, size limits, or interface residues to preserve. They define optimization goals by toggling ready-made templates (stability, binding, solubility, expression) or by entering a custom objective assembled from simple sliders (e.g., "raise ΔTm by 4–6 °C while keeping catalytic residues fixed"). After clicking Process, the platform performs three automated stages: (i) Structure proposal with RF-Diffusion + MPNN (<1 h) to produce diverse, constraint-aware backbones and initial sequences; (ii) Physics-aware screening via fast simulations (2–20 h depending on task) to score designs for stability, interface energy, aggregation, and basic manufacturability; and (iii) Active fine-tuning (~1.5 h) where a lightweight model adapts on-the-fly to the user's objective and the observed simulation feedback, improving the next batch of candidates.

The MVP's edge is a pretrained, customizable AI that fuses generative modeling with practical constraints. Out of the box it provides (a) functional analysis heuristics—motif checks, pocket quality, surface charge balance; (b) sequence generation guided by structure and editable residue locks; and (c) rapid fine-tuning that respects prior data while steering exploration away from mode collapse. This produces candidates that are not only high-scoring but also realistic to build and test.

Each job returns: top-N sequences/structures (FASTA and PDB), a one-page executive summary, and a detailed PDF with plots and tables. Default metrics include predicted ΔG/stability, interface ΔΔG (for binders), solubility/aggregation indices, disulfide and glycosylation alerts, and sequence diversity statistics. Users can sort by best score, Pareto-front trade-offs, or novelty vs. risk. A "Why this design?" panel cites the residues and features most responsible for the predicted gains.

The MVP runs in a secure, project-scoped workspace. Uploaded sequences are encrypted at rest; models are containerized and audited. We log only non-identifying usage statistics unless the team opts in to share wet-lab outcomes for model improvement. All generated content is versioned; users can reproduce any run with a single click.

The MVP prioritizes speed and clarity over encyclopedic modeling. We provide accurate ranking under well-defined conditions (e.g., pH, temperature, ionic strength) but do not claim full molecular dynamics replacement. Experimental validation remains with the user; however, the report includes cloning-ready sequences and constraints to accelerate bench work. Advanced options—multi-epitope design, de novo binder discovery against novel antigens, and multi-state objectives—are on the near-term roadmap.

Academic and startup teams that lack large in-house compute or ML expertise, but need iterative protein improvement with transparent reasoning. Typical use cases include stabilizing enzymes for process conditions, improving binder affinity while preserving specificity, or redesigning constructs for expression and solubility.

We judge MVP success by (1) time-to-candidate (<24 h for standard jobs), (2) user-rated interpretability of the report, (3) sequence diversity at fixed quality thresholds, and (4) early wet-lab hit rate from pilot partners. With this MVP, SEED & SEEK compresses months of trial-and-error into an actionable, auditable design loop that scientists can trust and iterate on immediately.

Protovate's intellectual property strategy forms the foundation of our long-term competitiveness and regulatory compliance. With both AI-driven reinforcement learning algorithms and directed evolution experimental workflows at the core of our innovation, robust IP protection is essential to secure exclusivity, prevent replication, and ensure sustainable commercialization in the global biotech landscape.

1. Freedom-to-Operate (FTO) Analysis

A comprehensive patentability search conducted in collaboration with NTHU's Intellectual Property and Technology Transfer Office (IPTTO) has yielded an 85% likelihood of successful approval, confirming the novelty and inventive step of Protovate's technology.

Our team has also performed a freedom-to-operate (FTO) assessment to ensure that Protovate's algorithms and protein optimization methods do not infringe upon existing AI-assisted protein engineering patents, such as those filed by DeepMind (AlphaFold) or Insilico Medicine. The FTO analysis highlights that Protovate's EcORep + SLCA integration—combining reinforcement learning optimization with directed evolution data—represents a distinct and patentable innovation not covered by existing filings.

2. Patents and Trademarks

Protovate is currently filing a U.S. provisional patent and an internal university patent through NTHU-IPTTO, covering:

• Reinforcement learning-based protein optimization algorithms.
• Experimental workflows for directed evolution validation.
• Novel data integration frameworks bridging computational and wet-lab design.

To strengthen market positioning, Protovate also plans to register trademarks for its brand name, visual identity, and proprietary platform labels. This ensures clear brand differentiation and prevents confusion or misuse in the emerging AI-biotech market.

3. Trade Secrets and NDAs

While key innovations will be patented, specific elements of Protovate's workflow—including model training datasets, parameter tuning strategies, and wet-lab validation methods—will remain protected as trade secrets.

To safeguard confidential data during collaborations, non-disclosure agreements (NDAs) are systematically signed with academic institutions, biotech partners, and service providers. This approach ensures that proprietary source code, generated protein data, and experimental results remain confidential and under controlled access.

4. IP Risk Mitigation and Regulatory Compliance

Protovate actively collaborates with patent attorneys and legal advisors specializing in biotech and AI regulation to ensure compliance with both U.S. and Taiwan IP law. Continuous monitoring of international patent databases allows us to update our filings and mitigate infringement risks proactively.

Beyond legal protection, Protovate's ethical and data governance framework aligns with global standards such as OECD biotechnology guidelines and AI transparency principles, ensuring responsible innovation and risk-free commercialization.

Protovate accelerates early-stage drug development over three times faster, cutting time and cost for pharma companies and research labs. Operating under a B2B licensing model, our sales rely on direct engagement from company officers with clear technical materials, not large marketing campaigns.

In Taiwan, we build trust through community networks, academic collaborators, and industry mentors, leveraging reputation and word-of-mouth to gain early adopters. By maintaining feedback-driven partnerships, Protovate ensures customer satisfaction and long-term loyalty while preparing for global expansion.

1. Direct B2B Engagement

• • Pitch decks & technical white papers to present PoC/MVP results to mid- to large-scale pharma and biotech companies
• • Executive-led meetings (CEO, CTO, CSO) to directly build trust without relying on a large salesforce

2. Academic & Industry Networks

• • iGEM and international conferences to showcase our technology and attract early partners and investors
• • Partnerships with NHRI, Academia Sinica, and NTHU Garage+ to strengthen credibility and validate our pipeline

3. Community & Reputation Building

• • Taiwan biotech & startup ecosystem via accelerators (Garage+, TTA) and startup competitions
• • Word-of-mouth & social exposure highlighting our "3x faster development cycles" as a key differentiator

4. Strategic Partnerships

• • Co-development with pharmaceutical companies to reduce entry barriers and demonstrate early clinical relevance
• • Licensing agreements to provide access to our core algorithm and protein optimization services, generating long-term revenue

1. Primary Customers

• • Mid- to large-scale pharmaceutical companies
  • 。 Need to accelerate protein/enzyme discovery and reduce cost in early-stage R&D.
  • 。 High demand for innovative platforms that can shorten preclinical timelines.
• • Biotech startups & research institutes
  • 。 Require efficient protein optimization but often lack in-house AI or high-throughput wet lab capabilities.
  • 。 Interested in licensing Protovate's platform for validation and proof-of-concept studies.

2. Secondary Customers

• • Academic research laboratories
  • 。 Particularly those in structural biology, protein engineering, and enzyme design.
  • 。 Value access to our AI-driven closed-loop system for faster, lower-cost experimental cycles.
• • CROs (Contract Research Organizations)
  • 。 Can integrate Protovate's platform into client pipelines as a service add-on.

3. Future Market Expansion

• • Agri-biotech companies → enzyme optimization for food and agriculture.
• • Industrial biotech firms → protein/enzyme design for green chemistry and materials.

Preparatory Phase (2025)

Development Phase (2026–2028)

Launch Phase (2029–2030)

Protovate envisions a long-term exit through an initial public offering (IPO) once the platform has established strong market traction and recurring revenue.

The IPO path is justified by three core milestones:

1. 1. Market Penetration and Licensing Revenue
   • • By 2028, Protovate aims to secure multiple biotech licensing agreements and co-development projects across Taiwan and Asia, generating sustainable cash flow through milestone payments and royalties (3–5% of partner drug sales).
2. 2. Global Expansion
   • • Following Series A, Protovate will expand into international biotech and pharma partnerships, strengthening its position as a unique platform provider in protein therapeutics.
   • • A diversified pipeline of licensing deals will reduce reliance on a single partner, building investor confidence.
3. 3. IPO Readiness
   • • With validated proof-of-concept, established patents, and recurring licensing + royalty income, Protovate will be positioned for listing on an international exchange (e.g., NASDAQ Biotech Index or Taiwan Stock Exchange biotech sector).
   • • The IPO provides liquidity for early investors while raising additional capital to scale globally and pursue broader therapeutic markets (oncology, immunology, industrial biotech).

Alternative Exits: In addition to IPO, Protovate remains open to strategic acquisition by a major pharma or biotech company seeking to integrate AI-driven directed evolution into their pipeline (e.g., Novartis, Roche, or AstraZeneca).