## 4 Discussion
[17] demonstrated how machine learning, particularly deep learning, is now capable of predicting experimental peptide measurements from amino acid sequences alone, a breakthrough that could dramatically improve the quality and reliability of analytical workflows in proteomics. Similarly, [PERSON] et al. [18] explored the use of AI and machine learning for protein toxicity prediction using proteomics data, further demonstrating the versatility of AI in handling complex biological datasets. Additionally, the study by [PERSON] et al. [19] focusing on protein-DNA/RNA interactions and machine intelligence tools, reveals the expanding scope of AI applications in understanding intricate molecular interactions. The escalating challenges in ensuring scientific integrity, particularly concerning the authenticity of WB images, have been thoroughly examined in recent studies by [PERSON] et al. and [PERSON] et al., both of which underscore the sophisticated threats posed by digital manipulations [20; 21]. [PERSON] al. revealed the efficacy of generative adversarial nets (GANs) in creating WB images so convincing that they are virtually indistinguishable from actual specimens. This discovery poses a profound challenge to traditional fraud detection methodologies, which primarily rely on visual inspections and pattern recognition but yield an accuracy barely above that of a blind guess. The research further demonstrated that detection accuracy does not significantly correlate with academic qualifications but shows a modest improvement with increased experience in WB-related research [20]. On a complementary note, [PERSON] et al. tackled the issue of WB image falsification by generating a comprehensive dataset containing over 14,000 real and 24,000 synthetic images through the use of GANs and denoising diffusion probabilistic models (DDPMs). Their findings corroborate the difficulty in distinguishing between authentic and counterfeit images, emphasizing that tools trained on genuine images can, indeed, detect forgeries. Moreover, their study highlights the resilience of these detection techniques against common image alterations such as compression, although challenges remain in accurately identifying images modified through scaling. However, synthetic images crafted using DDPMs were reliably identified post-resizing, marking a critical advancement in the protection of research integrity [21]. To address these emerging threats, [PERSON] et al. recommend the implementation of stricter verification measures, including the mandatory submission of WB images alongside a unique identifier generated by laboratory equipment and the peer review of these images in conjunction with article submissions. Such measures are proposed to bolster the verification process and mitigate the risks of scientific fraud, ensuring that the WB images presented in scholarly articles are authentic and trustworthy. Together, these studies illuminate the path forward in combating digital falsification, advocating for a combination of advanced detection technologies and rigorous review protocols to uphold the standards of scientific integrity [20]. From a broader perspective, our study highlights the potential of AI in complementing and enhancing research in molecular biology. The ability of AI models like Gemini, Gemini Advanced, Microsoft Copilot, and ChatGPT 4 to provide precise analyses in specialized scientific fields without specific training is a testament to the evolving versatility of AI technology. This opens doors to numerous applications where AI can assist in the interpretation of complex biological data, thereby accelerating research and discoveries in fields such as molecular biology, genomics, and proteomics. Furthermore, the successful application of these models suggests a future where AI could be specifically trained or fine-tuned using datasets from specialized fields such as WB analysis. Such advancements could lead to even more precise and nuanced interpretations, potentially revolutionizing the way scientists analyze experimental data.