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KYC / AML · Integrated
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OpenAI Partner · Certified
ISO 27001 · Certified
SOC 2 Type II · Compliant
Deloitte Fast 50 · Awarded
ERC-3643 · Compatible
KYC / AML · Integrated
MiCA-Ready · EU Compliant
VARA · UAE Licensed
OpenAI Partner · Certified
The Cost of Inaction
Every quarter without AI compresses competitor advantage in target selection, molecule design, and trial recruitment. Compounded across a pipeline, the gap is decisive.
See how blockchain-powered solutions eliminate the inefficiencies of traditional finance.
Drug discovery is full of unknowns, scattered data, and expensive decisions. Ment Tech helps pharma teams, biotech startups, and AI drug discovery companies bring more clarity into that process with AI systems built around real research needs, not generic automation.
We assist research teams in the identification of more robust disease targets by pooling biological data, scientific publications, omics datasets, and early research indicators into a unified workflow.
Bypass manual screening of enormous compound libraries with our models that you can narrow down molecules that have favorable indications to bind, safety, quality, and developability.
We build AI systems that help scientists explore new molecular structures, compare possibilities, and move beyond the limits of traditional compound search.
Our AI-driven drug discovery solutions help predict safety, toxicity, absorption, metabolism, and other key risks before teams spend heavily on lab validation.
We help improve promising candidates by analyzing molecular properties, activity patterns, risk signals, and optimization paths with clearer AI-backed recommendations.
We turn research papers, patents, trial data, and internal documents into searchable intelligence so scientists can find useful answers without digging through disconnected files.
We connect AI models with your existing lab tools, data systems, dashboards, and research workflows so insights are easy to use in everyday R&D decisions.
AI can speed up drug discovery, but it doesn't perform as well with dirty data, unreliable models, or a workflow that's separated from actual research. Ment Tech specializes in assisting pharma teams, biotech startups, and AI drug discovery companies to craft AI systems that are useful, interpretable, and more approachable for scientists in the lab.
Drug discovery data is distributed across lab instruments, published papers, spreadsheets, chemical libraries, and more. We unify that data so teams can operate from one consistent source of truth.
Weak data will give weak predictions. We prepare research datasets by cleaning, structuring, and enriching so your AI models can support better target, compound, and safety decisions.
Researchers need more than a prediction score. Our AI-driven drug discovery solutions are built with explainability so teams can understand why a model suggests a target, molecule, or risk signal.
If the training data is limited or unbalanced, the AI can miss important patterns. We add validation layers, review checks, and better data practices to reduce that risk.
AI must enable scientists, not replace their expert judgment. We build systems that provide enhanced evidence, time-saving analyses, and confidence before lab validation.
Many AI tools fail because they do not match how discovery teams actually work. Ment Tech builds solutions that connect with existing tools, dashboards, and lab processes.
Cloud & Platforms
Smart Contract Standards
Smart Contract Standards
Integrations & Partners
Technical Architecture
GxP is aligned with the unified data layer and discovery to the clinic continuum.
Every framework that governs pharma AI globally.
European Union
United States
United Kingdom
Singapore
UAE
Canada
Australia
21 CFR Part 11
FDA electronic records and electronic signatures
EU GMP Annex 11
EU computerized systems for GMP
FDA AI Discussion Paper
FDA AI in drug development principles
EMA AI Reflection Paper
EMA AI in medicine's life cycle reflection
ICH E6 GCP
Good clinical practice for trials
EU AI Act
High-risk AI for healthcare
GDPR Article 9
EU special category health data
HIPAA for RWE
Health data protection for RWE
Building AI for drug discovery is not just about adding a model on top of research data. It has to fit the way scientists work, the way decisions are made, and the way every result needs to be checked before moving forward. Ment Tech helps pharma teams, biotech startups, and ai drug discovery companies build AI systems that feel practical, reliable, and useful from the first stage of discovery.
Research Goals 1-2 Weeks
We begin by learning what your team is trying to improve, whether it is target discovery, compound screening, toxicity prediction, or lead optimization.
Data Preparation 1-2 Weeks
Research data is often spread across papers, lab systems, files, and compound libraries. We organize it properly so AI can work with stronger, cleaner information.
AI Method 2-3 Weeks
Not every problem needs the same model. We select the right mix of machine learning, deep learning, NLP, generative AI, or molecular modeling based on your use case.
First Prototype 4-8 Weeks
We create an early model or platform module that your scientists can test, question, and review before investing in a full-scale solution.
Trustable Results 2-4 Weeks
Drug discovery teams need to understand the “why” behind every prediction. We make outputs clearer so researchers can review targets, molecules, and risk signals with confidence.
R&D Integration Ongoing
Once the solution is validated, we connect it with your dashboards, lab tools, data pipelines, and research systems, turning it into a useful AI-driven drug discovery solution your team can use every day.
Case Study
Top 20 Global Pharma
Drug Discovery !
The Challenge
A leading pharma team had hit rates below 0.5% for a validated kinase target, while hit-to-lead cycles were taking 14 to 18 months. They needed AI-driven drug discovery solutions that could improve candidate quality and fit into Benchling and Schrödinger workflows.
Our Solution
Ment Tech built a generative chemistry platform with built-in ADMET filtering. It generated new scaffolds, shortlisted stronger candidates, sent them to Schrödinger for deeper analysis, and captured scientists' feedback in Benchling to improve future predictions.
8x ↗ vs virtual screening baseline
Hit Rate Lift
-58% ↗ from 14 months to 5.9 months
Hit To Lead Cycle Time
+340% ↗ vs library screening
Novel Chemical Space
+62% ↗ADMET filtering inline
ADMET Pass Rate
Defensible architecture for InfoSec, DPO, and internal audit review.
AI/ML security assessments
AI model security platform
AI risk management
AI red teaming services
Enterprise AI security
LLM API security testing
Prompt injection detection & prevention
LLM output filtering & content moderation
Role-based access control for AI endpoints
PII detection & automatic redaction
Hallucination detection & confidence scoring
Rate limiting & abuse prevention
Audit logging for all AI interactions
Model versioning & rollback capability
Adversarial input detection
Data residency & sovereignty controls
End-to-end encryption for sensitive prompts
Human-in-the-loop escalation workflows
Enterprise-Grade Security
Bank-level encryption and compliance standards.
256-bit AES encryption
99.99% Uptime SLA
24/7 Monitoring
See Our AI Solutions in Action
Get a personalized live demo tailored to your exact use case, built by the same engineers who will work on your project.
Why the top 20 pharma and biotech companies choose Ment Tech.
Ment Tech ships GxP-aligned pharma AI in 16 weeks with regulator-ready documentation.
ROI & Value
Measured impact across discovery, development, and post-market.
Key Metrics
generative vs virtual screening
AI site and patient matching
ADMET filtering inline
adverse event NLP
Discovery Compression
Hit to lead and lead optimization timeline compression.
20M to 200M per program
Trial Acceleration
Enrollment time reduction compresses time to market.
50M to 500M per asset
Post Market Efficiency
Pharmacovigilance and RWE automation
5M to 50M per year
Potential Annual Saving
Up to 70%
Engagement structures aligned to provider, payer, and digital health procurement.
Discovery And Architecture
Three-week feasibility, scoping, discovery, or development use case, regulatory posture, and ROI.
Pharma and biotech scoping a new AI program
Production AI Build
End-to-end build of a single pharma AI use case with GxP-aligned architecture.
Pharma and biotech, ready to ship
Discovery Platform Program
Multi-use case platform program across target, design, ADMET, and trial AI.
Top 20 pharma building an enterprise AI platform
What's Included in Every Engagement
Get Your Tailored Project Quote
Share your requirements and receive a detailed technical proposal with transparent pricing within 48 business hours.
FAQ
AI drug discovery platforms allow pharma and biotech teams to utilize their research data more efficiently. They can be used for pre-laboratory validation, early discovery work, such as target identification/validation, screening, safety signal detection, or lead optimization.
Artificial Intelligence can analyze the vast amount of biological data, chemical structures, laboratory data, and scientific literature faster than research by hand. This allows researchers to identify relevant trends, eliminate poor candidates early, and work with the best options in the end.
No. AI cannot replace scientists or lab validation. It works best as a support system that gives researchers stronger insights, faster analysis, and clearer direction while scientists still make the final calls.
It depends on the project, but common data includes compound libraries, assay results, omics datasets, lab notes, patents, research papers, and preclinical or clinical data. Ment Tech helps clean and connect this data so the AI can produce more reliable results.
Still have questions?
Can’t find the answer you’re looking for? Our team is here to help.
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Book a Pharma AI Strategy Session. We will scope your discovery or development use case and regulatory posture and ship a compliant blueprint within one week.