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Technology companies are introducing artificial intelligence and machine learning techniques to accelerate drug discovery and development.
FREMONT, CA: Artificial intelligence (AI) has the potential to make waves in drug discovery. However, it is not without its hurdles. Given its interdisciplinary character, drug discovery has always been driven by novel developments, whether in the physical sciences or in the biological sciences, when applying computational approaches to the field. Here is a look at how AI is being utilized throughout the drug design and development processes.
The new pharmaceutical discovery and development platform is developed to harness artificial intelligence (AI) and machine learning to offer advanced analytics necessary for informed decision making. The data-agnostic platform allows users to import, integrate, process, link, visualize, break down information from several sources, and bring it into a common reference frame. The AI platform allows users to harness diverse data assets to arrive at new insights, develop predictive foresight and seamlessly share findings with stakeholders.
The need to offer innovative therapeutics more cost-effectively and create profitable growth means that drug developers need to adopt disruptive technologies. Being able to fail faster, design optimized, targeted, and efficient clinical trials and boost the discovery and approval of new therapeutics while mitigating cost is achievable with the AI. Researchers can leverage the platform to process many datasets simultaneously and at scale, freeing them up for other valuable tasks. The platform can be integrated seamlessly with ELNs and LIMSs, in-house data lakes, single-point-of-truth for experiment meta-data, and public data sources, like TRON, TCGA, and GTeX.
The AI platform is configurable and modular and can be designed to cater for several data types and business requirements. Its experiment suites, which remediate specific challenges and are focused on one type of scientific data, are mapped to organization-wide sets of samples, vocabularies and ontologies, allowing a centralized, accessible, auditable repository of data, analysis, machine-learning models and reports. It also provides a framework by which governance, regulatory and security policies can be applied, and insights can be generated in real-time. Ultimately, the AI platform combines machine learning and human expertise to facilitate the effective creation of better drugs.
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