Affinity improvement of a therapeutic antibody anti cancer drug trast uzumab by sequence based computational design

Abstract

Artificial intelligence (AI)/Machine learning (ML) has newlineapplications in almost all industries, from computer applications to newlinebiotechnology. One of the fascinating sciences is bioinformatics. newlineBioinformatics concentrates on applying computer science and techniques of newlineartificial intelligence in biology. With the ongoing COVID-19 pandemic and newlinethe recent Ebola outbreak, antibodies and vaccine development has become newlinethe need of the hour. The development of antibodies and vaccines is newlinesignificant to prevent the spread and control the outbreak. Nevertheless, newlinedesigning antibodies and developing the vaccine is very long and expensive, newlinecontaining lots of trials and errors methods. In addition, the most lifethreatening newlinedisease, such as cancer, has a whopping 10 million deaths in 2020 newlineaccording to the World Health Organization (WHO). More accurate and rapid newlineantibody identification and modification of existing antibodies through newlinecomputational approaches is the most pressing need for cancer. newlineThe newly approved antibody-based therapeutics are rapidly increasing newlinewith more than 100 new mAb Food and Drug Administration (FDA) newlineapprovals with multiple antibodies in clinical trials and patent filing stages. newlineThis is reflected in the market size for these molecules, estimated at USD130 newlinebillion in 2020 and projected to grow to USD223 billion by 2025. Most of newlinethese antibodies on the market were developed using costly and timeconsuming newlinetechniques, chiefly phage display or animal immunization newlineplatforms. With the maturity and increasing integration of computational newlineprotocols like machine learning, within pharma company pipelines, the time newlineand cost associated with therapeutic antibody development are expected to newlinedecrease. newline