Unveiling the Molecular Code: Antibody Sequence Mining and Target Affinity Analysis

Our client’s requirement

The client is at the forefront of drug discovery, leveraging advanced artificial intelligence to reshape the drug development landscape and deliver enhanced global patient care. Their AI-driven workflow platform spans from target identification to lead generation, providing insights for developing commercially viable drugs through in-house and collaborative projects. With a proven track record in AI-driven drug discovery, the company expedites the process using its proprietary technology platform to introduce novel treatments for complex diseases.

The customer has a critical need for a comprehensive training set specifically tailored to therapeutic monoclonal antibodies (mAbs) and associated structure-activity relationship (SAR) data. The primary objective is to harness this dataset to enhance machine-learning algorithms, enabling the identification of new targets within the intricate field of immuno-oncology. However, the current absence of a specialized training set aligned with their unique requirements poses a significant challenge. The lack of this dataset has profound consequences, including limited target identification, compromised AI/ML performance, missed opportunities, and delayed innovation.

Our approach

With over 60 PhDs in our data curation team, we have the domain expertise required to identify the relevant literature, extract the appropriate data, and deliver it in a standardized, analysis-ready format. Our curation process includes three stages of data extraction: manual curation, review, and quality control. With our combination of scientific expertise and technical excellence, we were able to collate, prepare, and deliver the client’s data set in an exceptionally short time. The manually extracted data included exemplified sequence details and associated experiments, reported for a variety of assays. With our assistance, the client was able to swiftly proceed with the ongoing research program, avoiding the bottleneck of the data collection phase.