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About usHealthcare & MedtechPharma & Biotech
AI is transforming the pharmaceutical industry, and one of the most significant areas of impact is in the drug discovery process. Using advanced machine learning algorithms, such as transformer models and graph neural networks, and vast amounts of data, AI is accelerating the discovery of new treatments and improving the efficiency of the entire development process.
Multiomics data analysis
Clinical data analysis
Scientific research data analysis
De novo drug design
ML + molecular dynamics
ML + molecular docking
Improve your AI-driven drug discovery with Innowise.
AI is a total change-maker in the pharmaceutical industry: it offers lots of benefits that make the drug discovery and development process smoother and more efficient.
We developed a machine learning model to predict aqueous solubility for novel small molecule inhibitors targeting protein X. Using experimentally determined solubility data, we trained a custom ML pipeline leveraging molecular descriptors. The model achieved an R-squared of 0.70 on external validation, making it a valuable tool for screening virtual libraries and prioritizing compounds for synthesis.
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We improved hepatic clearance predictions in a GastroPlus PBPK model using a hybrid machine learning approach. Combining LightGBM and D-MPNN graph neural networks, our model achieved an R-squared of 0.82 in cross-validation. The integration reduced average fold error from 2.5 to 2.0 compared to traditional in vitro scaling methods, enabling more reliable drug exposure predictions.
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Before designing a drug, we need to pinpoint the therapeutic target – whether it's a specific enzyme, a mutated gene, or a critical signaling pathway. Through analysis of large-scale biological data, including genomic and transcriptomic information from next-generation sequencing (NGS), AI helps identify the best therapeutic targeting opportunities. Advanced machine learning algorithms, such as graph neural networks and transfer learning.
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AI isn't a replacement for traditional methods in drug development. It's a great tool to help speed things up and make them more efficient. While AI algorithms can analyze huge amounts of data, predict molecular properties, and identify potential drug candidates more efficiently than traditional approaches, it's still important to test them out in real life.
Innowise's AI projects are built to comply with all relevant regulatory standards (FDA, EMA, ICH, GDPR, HIPAA). We have rigorous data governance, validated and documented models, and a commitment to explainable AI. We make sure that the data we use is of good quality, secure, and transparent throughout the development process. This thorough process helps us avoid potential risks and guarantee reliable results for our clients.
Proficient in machine learning (deep learning, reinforcement learning, classical techniques), cheminformatics, bioinformatics, and drug development processes, Innowise leverages industry-standard tools and technologies to create impactful AI solutions for drug discovery.
You can hire AI developers from Innowise by contacting our team through our website. We offer flexible engagement models, including project-based contracts and dedicated teams, to best suit your project needs and budget and assemble a team of AI developers with the right expertise to deliver successful outcomes.
