Bioscience Method 2024, Vol.15, No.1, 28-36 http://bioscipublisher.com/index.php/bm 34 3.4 Computational resource requirements Deep learning models, particularly those with many parameters, demand substantial computational resources. This includes significant processor (CPU or GPU) capabilities and extensive memory requirements. For some research institutions and small businesses, the high computational costs may limit their ability to use deep learning for predicting drug molecule activity. Moreover, the training and optimization processes of complex models can be time-consuming, which might become a bottleneck in research and development environments that require rapid iterations and experiments. Therefore, finding more efficient models and algorithms with lower computational costs is a crucial direction in current deep learning research. By addressing these challenges, the field can better leverage deep learning technologies to advance drug discovery processes effectively. 4 Conclusion and Prospects Deep learning technology has shown tremendous potential and significant contributions in predicting the activity of drug molecules. By utilizing advanced algorithms such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), researchers can extract valuable features from complex molecular structures to predict the activity of drug molecules. These methods play a crucial role in accelerating drug discovery, reducing research and development costs, and enhancing prediction accuracy. In particular, the development of self-supervised learning frameworks like ImageMol has provided a new approach to processing unlabeled data, further expanding the application range of deep learning in drug design. To fully leverage the potential of deep learning in drug discovery, interdisciplinary collaboration is strongly recommended. The close cooperation between chemists, biologists, and computer scientists can accelerate the process of discovering new drugs by sharing knowledge, data, and resources to jointly address challenges in drug design. Such collaboration can help develop more accurate and interpretable deep learning models, thereby improving the practicality and transparency of the models. Promoting communication and collaboration among scientists from different backgrounds will provide new perspectives and solutions for solving complex problems in drug discovery. Future research should focus on enhancing the interpretability, generalization ability, and data efficiency of deep learning models. Specifically, developing new model interpretation tools will help researchers understand the molecular features and biological mechanisms behind model predictions, thereby increasing the transparency and trustworthiness of the models. Improving the models' generalization capability will ensure that deep learning algorithms maintain high performance across different chemical spaces and biological environments. Technically, combining deep learning with cutting-edge technologies like quantum computing and augmented reality may open up new research directions in simulating complex molecular dynamics, exploring unknown chemical spaces, and designing personalized drugs. Developing algorithms that can effectively utilize small or imbalanced data will also be a key focus of future research, which is particularly important for accelerating the development of drugs for rare diseases and personalized medicine. Deep learning offers new tools and methods for predicting drug molecule activity and discovering new drugs. Through interdisciplinary collaboration, continuous improvement of deep learning technologies, and exploration of its new applications in drug discovery, a more efficient, precise, and personalized drug development process is expected in the future. References Amabilino S., Pogány P., Pickett S., and Green D., 2020, Guidelines for RNN transfer learning based molecular generation of focussed libraries, Journal of Chemical Information and Modeling, 60(12): 5699-5713. https://doi.org/10.1021/acs.jcim.0c00343 PMid:32659085
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