Immunogenicity prediction is a central topic in reverse vaccinology for finding candidate vaccines that can trigger protective immune responses. Existing approaches typically rely on highly compressed features and simple model architectures, leading to limited prediction accuracy and poor generalizability. To address these challenges, we introduce ProVaccine, a novel deep learning solution with a dual attention mechanism that integrates pre-trained latent vector representations of protein sequences and structures. We also compile the most comprehensive immunogenicity dataset to date, encompassing over 9,500 antigen sequences, structures, and immunogenicity labels from bacteria, viruses, and tumors. Extensive experiments demonstrate that ProVaccine outperforms existing methods across a wide range of evaluation metrics. Furthermore, we establish a post-hoc validation protocol to assess the practical significance of deep learning models in tackling vaccine design challenges. Our work provides an effective tool for vaccine design and sets valuable benchmarks for future research.

When starting a vaccine program, scientists generally have anecdotal understanding of the disease they're aiming to target. When Covid-19 surfaced over a year ago, there were so many unknowns about the fast-moving virus that scientists had to act quickly and rely on new methods and techniques just to even begin understanding the basics of the disease. Scientists at Janssen Research & Development, developers of the Johnson & Johnson Covid-19 vaccine, leveraged real-world data and, working with MIT researchers, applied artificial intelligence and machine learning to help guide the company's research efforts into a potential vaccine. "Data science and machine learning can be used to augment scientific understanding of a disease," says Najat Khan, chief data science officer and global head of strategy and operations for Janssen Research & Development. "For Covid-19, these tools became even more important because our knowledge was rather limited. There was no hypothesis at the time. We were developing an unbiased understanding of the disease based on real-world data using sophisticated AI/ML algorithms."

When starting a vaccine program, scientists generally have anecdotal understanding of the disease they're aiming to target. When COVID-19 surfaced over a year ago, there were so many unknowns about the fast-moving virus that scientists had to act quickly and rely on new methods and techniques just to even begin understanding the basics of the disease. Scientists at Janssen Research & Development, developers of the Johnson & Johnson-Janssen COVID-19 vaccine, leveraged real-world data and, working with MIT researchers, applied artificial intelligence and machine learning to help guide the company's research efforts into a potential vaccine. "Data science and machine learning can be used to augment scientific understanding of a disease," says Najat Khan, chief data science officer and global head of strategy and operations for Janssen Research & Development. "For COVID-19, these tools became even more important because our knowledge was rather limited. There was no hypothesis at the time. We were developing an unbiased understanding of the disease based on real-world data using sophisticated AI/ML algorithms."

Biomedical research of vaccines against COVID-19 was already being tested in humans in March. Three months after the initial outbreak was identified in China, many of those owed their rapid start to the power of Artificial intelligence (AI). The feat is a promising and remarkable step in more than 200 years of immunization history. The experience may revolutionize the way vaccines are developed, potentially saving countless lives in future epidemics. According to the World Health Organization (WHO), 34 vaccine candidates were being tested in humans as of early September.

A team of researchers hailing from Harvard and Université de Montréal today launched Epitopes.world, It's built atop an algorithm -- CAMAP -- that generates predictions for potential vaccine targets, enabling researchers to identify which parts of the virus are more likely to be exposed at the surface (epitopes) of infected cells. Project lead Dr. Tariq Daouda, who worked alongside doctorates in machine learning, immunobiologists, and bioinformaticians to build Epitopes.world, Fewer than 12% of all drugs entering clinical trials end up in pharmacies, and it takes at least 10 years for medicines to complete the journey from discovery to the marketplace. Clinical trials alone take six to seven years, on average, putting the cost of R&D at roughly $2.6 billion, according to the Pharmaceutical Research and Manufacturers of America.

According to experts, there is very little chance a vaccine for COVID-19 will be perfected and ready for use this year. This eagerly sought goal might take place by 2021 at the earliest -- but only if things proceed smoothly with the 60 vaccine trials currently taking place. The latest expert source to attest to this impossibility used its experience in quantitative financial investment to analyze the progress being made by the 60 vaccine candidates. Boston-based PanAgora Asset Management analyzed vast quantities of medical research data to calculate which of the 60 will succeed in producing a successful vaccine within the year. The quick answer is "zero."

The novel coronavirus has been circulating among humans for barely three months, but several bio-tech firms have already created drugs that target the COVID-19 disease. One of the secret weapons for the fast response is artificial intelligence. The Chinese government initially was criticized for downplaying the severity of the coronavirus outbreak that originated in Wuhan last December. However, researchers around the world applauded the quick work of Chinese scientists in decoding the genetic sequence of the virus, dubbed SARS-CoV-2, and posting the results in a public database on January 10. Researchers quickly went to work.