Lead AI builds governed quality workflows and predictive AI models for pharmaceutical teams, from drug discovery to manufacturing. Our pharma AI software helps teams turn complex pharmaceutical data into reliable intelligence that can support scientific and operational decisions.
Built with researchers atNUSA*STARITB
Our team are scientists first, with experience across pharmaceutical R&D and quality, as well as data science, machine learning, and AI. We use that understanding to build working systems for pharmaceutical teams, from governed GxP workflows to predictive models over molecules and assays.
Our systems can run on your infrastructure or ours, depending on your requirements.
Pharmaceutical data is rarely ready for AI out of the box. Experimental procedures can produce different measurements, important information can sit in scientific narratives and scanned records, and poor preparation can introduce bias or leakage into a model.
Before AI can learn from pharmaceutical data, the underlying data needs to be prepared, structured, and traceable.
Lead AI's Selective Cleaning approach accounts for differences between experimental procedures rather than treating conflicting measurements as interchangeable. It standardises structures and units, resolves duplicates, handles unstructured sources, and preserves the evidence behind the data.
Every dataset leaves the preparation pipeline with a data card documenting its origin, what was done, test split, and version.
AI data readiness is more than cleaning a dataset. For pharmaceutical applications, preparation needs to account for how measurements were produced, where information came from, how conflicting values are resolved, and how the data is divided for model evaluation.
Research institutions, partners and programmes.
Each cell ≈ 1% of the library.
Shortlist shown schematically.
01 / SEARCH SPACE
candidate peptides
A combinatorial library is enumerated around the target — 603,750 candidates.
A twelve-residue peptide has roughly four quadrillion possible sequences, making exhaustive testing impractical. We enumerate a chemically sensible library, use physics-based simulation on an affordable sample to generate real labels, train a model on those labels, and score the remainder. Successive filters and independent methods narrow the field before anything reaches the lab.
603,750 peptides screened from 6,037 simulated candidates — 1% of the library simulated
Lead AI uses predictive modelling to rank candidates and focus experimental attention on the most promising options, while making uncertainty visible where the model cannot provide a reliable ranking.
Connect deviations, causes, actions, and verification to identify recurring problems across quality records and support structured investigation and root cause analysis.
We are a build partner, not a shrink-wrapped tool. Our scientists design and build the systems your team actually needs, from governed quality workflows and predictive models to the data foundation underneath them, or all of it end to end.
Every engagement can run on your infrastructure or ours, depending on your requirements.
Governed GxP workflows for the processes inspectors ask about.
A case moves through controlled stages with electronic signatures and an immutable audit trail underneath, giving teams a structured and traceable way to manage regulated processes.
Custom models over molecules and assays.
We build predictive models for target and activity prediction, antigen and construct design, screening, ADMET, and yield, bringing together pharmaceutical expertise with data science and machine learning.
Selective Cleaning turns complex pharmaceutical data into validated assets.
Lead AI's Selective Cleaning approach addresses multi-lab and multi-assay data before it reaches a model, helping create a consistent, traceable foundation for AI development and scientific review.
From data mining to a deployed and validated system.
We support the full journey from data preparation and model development through deployment and validation, on your servers or ours.
Stream prepares data. Lattice connects the evidence. Elyndra reasons over it.
Stream standardises, cleans, and documents pharmaceutical datasets while preserving the evidence behind each dataset.
Prepared, traceable data
Lattice combines meaning-based retrieval with a knowledge graph to connect events, causes, actions, and verification.
Relevant evidence with citations
Elyndra brings pharmaceutical terminology and domain reasoning to quality and manufacturing workflows, using the evidence retrieved by Lattice.
Reasoning grounded in evidence
Elyndra reasons. It does not remember your facts.
Facts come from Lattice with citations. Client data does not train the model.
Lead AI grew out of research into data quality for molecular AI. Today, we build technology for pharma companies, government agencies, and research institutions across Asia-Pacific.
CEO & Chief Scientist
PhD candidate at NUS. Inventor of Selective Cleaning. Published researcher.
Chief Operating Officer
Master of Venture Creation at NUS. Leads commercial strategy and operations.
Scientific Advisor
Senior researcher at NUS. Global citation index of 100,000+. Provides scientific oversight.
Our work is peer reviewed and published. We make our methods available for independent evaluation and reproducibility, with results reported against published benchmarks.
21.6%
Selective Cleaning reduced model error by 21.6% compared with standard data preparation on a published oncology benchmark.
R² = 0.87 · Performance reported on an MDM2 oncology benchmark
Peer reviewed
The underlying data preparation method has been peer reviewed and published, with the research and evaluation methods available for independent review.
Reproducible · The underlying pipeline has been publicly released for reproducibility
200+
Our research has been presented at international scientific and technical conferences, including the Asia-Pacific Theoretical and Computational Chemistry Conference 2025 and ACM AI and HPC 2026.
Researchers reached directly through research presentations and outreach
Pharmaceutical AI needs more than performance. It needs traceability, governance, and accountability.
Client data is used at query time and is never used to train the model.
Information retrieved from your organisation's knowledge base can be traced back to its source.
AI supports the workflow. Accountability remains with the people responsible for the decision.
Deploy according to your infrastructure, security, and governance requirements.
Validation is designed in from the first sprint, not added before go-live.
*Certification targeted for 2027.
Explore our latest research, publications, collaborations, and developments in pharmaceutical AI.
[ News · 18 Feb 2026 ]
We have spent the last decade obsessed with the "brain": the model. The real barrier to AI discovery sits upstream, in the data.
Read the article →[ News · 18 Feb 2026 ]
The pharmaceutical industry is currently navigating its most significant transformation since the dawn of the antibiotic era.
Read the article →[ News · 18 Feb 2026 ]
The pharmaceutical industry has long been haunted by Eroom’s Law: while computing gets cheaper every year, drug discovery keeps getting slower and more expensive.
Read the article →Share your data challenge and we'll show you how Selective Cleaning can strengthen the data foundation behind your pharma AI software and pharma AI platform.
Bring your challenge.
Let’s find the right next step.
