AI Solutions for Drug Discovery and Development

How can AI and machine learning accelerate drug discovery?

AI and ML transform the drug discovery process by automating data-heavy, time-consuming steps that traditionally take years. Our models mine multi-omics datasets, scientific literature, and real-world evidence to uncover new therapeutic targets with higher confidence. Virtual screening and de novo molecular design enable rapid hit discovery and lead optimization by predicting binding affinities, ADMET properties, and toxicity profiles before costly lab synthesis. In preclinical and clinical phases, AI improves trial design, patient stratification, and real-time safety monitoring, significantly boosting success rates.

Do I need my own datasets to start using AI for drug discovery?

Not necessarily. We can work with your proprietary experimental or clinical datasets, but also integrate publicly available biomedical data such as genomics, proteomics, transcriptomics, and chemical libraries. Our team specializes in data engineering: cleaning, harmonizing, and merging structured and unstructured sources into usable formats. We also design cloud-based data lakes and pipelines that enable ongoing ingestion of lab results, literature, and real-world evidence.

Is your software compliant with industry regulations?

Yes. Every solution is built with regulatory compliance in mind. We follow global standards such as FDA 21 CFR Part 11, EMA guidance, HIPAA, GDPR, and GxP practices (GLP, GCP, GMP). Our processes include full audit trails, explainable AI modules, and validation protocols that align with regulatory submission requirements. For pharmacovigilance and clinical trial systems, we also support integration with CTMS and EDC platforms, ensuring seamless compliance in regulated R&D environments.

What kind of companies benefit from these solutions?

Our AI/ML services are designed for the entire life sciences ecosystem. Large pharmaceutical enterprises use them to accelerate discovery pipelines and improve trial efficiency. Biotech startups rely on us to scale quickly without building in-house infrastructure, especially for target discovery and lead optimization. Contract research organizations (CROs) adopt AI to expand their service offerings and gain efficiency in outsourced R&D. Academic research institutions and government labs use our solutions for multi-omics research, biomarker discovery, and translational studies.

How do you handle data privacy and security?

We apply enterprise-grade security across all projects. This includes end-to-end encryption of data in transit and at rest, strict access controls, role-based permissions, and secure cloud or hybrid deployment options. Our infrastructure and workflows are aligned with ISO 27001, GDPR, and HIPAA standards. For highly sensitive research, we design validated computer systems that satisfy regulator expectations for auditability and traceability. Protecting patient confidentiality and safeguarding proprietary IP are central to our engagement model.

Can AI completely replace lab experiments?

No, AI is not a substitute for bench science, but a powerful accelerator. It narrows down the vast chemical and biological space to a manageable number of high-probability candidates, reducing trial-and-error and wasted resources. For example, AI-driven QSAR and ADMET predictions help you avoid synthesizing molecules likely to fail due to toxicity or poor bioavailability. Final validation still requires in vitro, in vivo, and clinical studies, but AI ensures those efforts are focused on the most promising candidates.

How long does it take to build a custom AI/ML solution for drug discovery?

Timelines depend on data availability, model complexity, and project scope. A proof-of-concept model, such as a virtual screening pipeline or a toxicity classifier, can often be delivered in a few weeks. More comprehensive platforms, including data integration layers, predictive dashboards, and regulatory compliance features, typically take several months. Our iterative approach means you start seeing value quickly, while we continue to expand capabilities in parallel.

Do you offer consulting in addition to software development?

Yes. Innowise provides both advisory and technical services. We start with feasibility assessments, AI strategy workshops, and proof-of-concept designs to validate the business case. Once value is clear, we build, deploy, and maintain end-to-end AI systems, including MLOps pipelines, cloud infrastructure, and integrations with lab and clinical systems. We also embed domain specialists into client teams, offering flexible outstaffing of data scientists, bioinformaticians, and ML engineers to support your in-house R&D.