At present, a serious problem facing the world is the lack of antifungal drugs and the increasing resistance of existing drugs. Fungal infection has become one of the serious threats to public health, especially in the immunocompromised patient population, the complications caused by fungal infection have a high fatality rate. However, the range of antifungal drugs available is limited, and many traditional drugs have failed or are not effective. Therefore, the development of new antifungal drugs is an urgent need in the medical field.

Our team's goal is to rapidly screen small molecule peptides with antifungal activity through advanced machine learning techniques, especially the application of the DeBERTa model. This process is mainly based on qualitative and quantitative analysis of a large number of known biological sequences in the NCBI database. Through pre-training of machine learning models and innovative algorithms for predicting antifungal activity, we were able to screen potential drug candidate peptides with higher precision. This approach is more efficient and cost-effective than traditional laboratory screening techniques, and can quickly produce a large number of candidate peptides that can be used for further study.

The preliminary screening of short peptides with potential antifungal activity will become the basis of our drug development work. In subsequent studies, we will optimize the molecules of these candidate peptides to improve their efficacy and stability in vivo. By combining structural biology and chemical synthesis techniques, we can conduct in-depth analysis of the structure-activity relationship of these peptide molecules to optimize their antibacterial effect and reduce possible side effects.

This step is crucial for drug development to ensure that the short peptides we have selected not only perform well in vitro, but also have stable antifungal effects in the in vivo environment. By optimizing the lipophilicity, molecular size and stability of peptide molecules, we are expected to develop potent drugs with broad-spectrum antifungal activity that can span multiple fungal pathogens.

Our antifungal drug development program is not only at the basic research stage, but also aims to promote clinical translation. After optimizing the candidate peptide, we plan to conduct a series of in vitro and in vivo experiments to verify its safety and efficacy. This will include cytotoxicity testing, animal studies and subsequent human clinical trials. After ensuring the safety and effectiveness of the drugs, we will seek cooperation with pharmaceutical companies to promote the market application of these new drugs.

This method of screening antifungal drugs through machine learning technology not only greatly accelerates the drug development cycle, but also provides innovative solutions for the treatment of fungal infections. We expect these drugs to provide safer and more effective treatment options for at-risk groups such as immunosuppressed patients, tumor patients, and organ transplant patients to help them fight difficult-to-treat fungal infections."

With the increasing resistance of antifungal drugs, the global demand for new antifungal drugs has shown explosive growth. The short peptide drugs we have developed will fill a gap in the existing market and bring new light to the treatment of fungal infections." Due to the advantages of short peptide drugs such as small molecules, easy synthesis and strong modifiability, they have broad market prospects, not only can be used to directly treat infections, but also may be developed into preventive drugs to help susceptible people resist fungal invasion.

In addition, the broad spectrum activity and low side effects of this class of drugs give them great potential for commercialization. Our team's screening platform can also be extended to the development of other antimicrobial agents to aid in the treatment and prevention of multiple pathogen infections. This approach is expected to significantly reduce the prevalence and mortality of fungal infections worldwide, with positive implications for public health.