Services
Application of multimodal models and graph neural networks to accelerate drug discovery, protein structure prediction, enzyme design, and automated生物系统设计 at digital speed. Sub-services include AI for small molecule drug discovery, CRISPR-GPT integration services, generative AI for enzyme engineering, and computational biology foundation model development.
Development of AI systems that generate novel, stable, and functional protein sequences for therapeutic, industrial, and diagnostic applications, moving beyond prediction to de novo creation.
Architecture of end-to-end computational platforms that integrate generative AI, molecular simulation, and high-throughput screening data to accelerate small molecule and biologic lead identification.
Engineering of AI platforms that design optimal CRISPR guides, predict off-target effects, and analyze high-content screening data to massively accelerate functional genomics and therapeutic development.
Strategic guidance and technical implementation for training or fine-tuning large-scale, multimodal foundation models (e.g., ESM, AlphaFold) on proprietary biological data for competitive advantage.
Specialized adaptation of pre-trained biological AI models (for structure, function, interaction) to specific organism, disease, or experimental data contexts to achieve lab-validated accuracy.
Construction of robust, scalable data ingestion, featurization, and model deployment pipelines for heterogeneous biological data types (omics, imaging, text) ensuring reproducibility and compliance.
Implementation and customization of state-of-the-art deep learning systems (like AlphaFold, RoseTTAFold) for predicting protein, RNA, and complex structures with atomic-level accuracy for R&D.
Design of AI workflows that generate and optimize enzyme variants for improved catalytic activity, substrate specificity, and stability in industrial bioprocesses and green chemistry.
Integration of machine learning with robotic liquid handlers, high-content screeners, and other lab hardware to create closed-loop, autonomous experimentation systems that design, run, and analyze experiments.
Creation of high-fidelity, privacy-preserving synthetic datasets for genomics, proteomics, and clinical trials to overcome data scarcity, accelerate model training, and ensure regulatory compliance.
Development of AI systems that integrate multi-omic patient data (genomic, transcriptomic, proteomic) with clinical records to identify biomarkers, predict treatment response, and enable personalized therapeutic strategies.
Application of machine learning to optimize patient recruitment, site selection, and trial design, using predictive analytics to reduce costs, timelines, and attrition rates in pharmaceutical development.
Engineering of GNN-based AI to model complex biological interactions (protein-protein, gene regulatory, metabolic pathways) for drug target identification, polypharmacology, and systems biology insights.
Development of AI systems that jointly analyze and interpret disparate biological data modalities (text literature, imaging, sequencing, sensors) to uncover novel, holistic insights for research and development.
Services to ensure AI/ML models used in drug discovery, diagnostics, and clinical decision support are developed, validated, and documented to meet FDA, EMA, and ISO 13485 regulatory standards for market approval.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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