Drug development is a complex process and often takes 10-15 years and billions of dollars to bring a new drug to market, but fewer than 12% of drugs even make it through clinical development. It typically takes 10-15 years and billions of dollars to bring a new drug to market, but fewer than 12% of drugs even make it through clinical development.
Drug Discovery & Development Services provide solutions aimed at minimising the barriers faced by pharmaceutical companies. The goal of our service offerings is to drive innovation in pharmaceutical R&D by focusing on expediting drug design and development through the prediction of clinical trial success
Solutions with speed
With advances in high-throughput technologies and data management systems, there are now vast datasets to be applied in the field of biomedicine to provide more in-depth insights needed to fail early and fast.
With the ability to draw from our vast database of approved and non-approved compounds, our machine learning engineers and data scientists produce solutions that shorten the development cycle.
Drug designing is a process that involves the combination of humans, machines and advanced AI to generate new and stable molecular compounds. Drug Discovery & Development Services uncover new opportunities for companies and help them adapt to challenges with speed, agility, and confidence.
Drug Discovery & Development Services include:
Molecular Design & Optimization
With improved accuracy and speed, computational methods rooted in deep learning are leveraged to automate the molecular generation process for both identifying novel targets and designing new lead compounds. Targets are validated by identifying interactions early in the discovery process with lead target interaction, and by comparing with research literature using natural language extraction (NLE).
Biomarker Identification
AI-powered biomarker identification and validation support R&D by identifying potential patients through prognostic and predictive markers to better profile patient subpopulations ahead of trial start-up. Biomarkers include demographic, genetic and clinical data points. Biomarkers of interest are identified and validated through innovative approaches, such as deep computational phenotyping, to aid in patient stratification and increase knowledge of disease progression. Utilising patient subpopulations derived through biomarker identification can increase trial success while lessening enrollment timelines and reducing costs
Drug Repurposing
Repurposing drugs is one of the most effective ways to shorten development timelines and improve clinical trial success. By mining existing and failed compounds, assets and drugs for new uses, pharmaceutical companies can focus solely on the clinical testing portion of the approval process and save vast amounts of clinical development time and money.
Computational approaches are being developed to improve the effectiveness of existing drugs by extracting optimum drug combinations from literature, mining electronic medical records (EMR) and claims data, and making use of advanced NLP techniques.
Drug repurposing is the process of finding new, more powerful drugs from old ones. It’s a complex problem and almost as difficult as drug design, but we can build drug repurposing in such a way that it can look at the available chemical space — our approaches are disease and treatment agnostic.
We can run pipelines for drug property, drug-to-drug interaction, drug target interaction, protein function, and protein-to-protein interaction predictions. All with the goal of augmenting the speed, accuracy, and cost of conducting these processes. NP Test is a qualitative assessment tool that measures the effectiveness of your drug.