Bioscience Method 2024, Vol.15, No.1, 28-36 http://bioscipublisher.com/index.php/bm 32 benchmark set. Karimi et al. (2018) in their study, they proposed a semi-supervised deep learning model that combines recurrent neural networks and convolutional neural networks. This model can use both unlabeled and labeled data for joint encoding of molecular representation and affinity prediction. Their approach achieved a relative error within a factor of five in test cases, and up to a factor of twenty for proteins not included during training, showcasing its high accuracy and interpretative capability. Öztürk et al. (2018) introduced the DeepDTA model, which uses convolutional neural networks (CNNs) to predict the binding affinity between drugs and proteins. This method utilizes the one-dimensional sequence information of drugs and targets, displaying good predictive performance. The main innovation of the DeepDTA model lies in its ability to effectively predict their binding affinity without the need for three-dimensional structural information of the drug and target. This method not only achieved performance superior to existing techniques on a larger benchmark dataset but also its use of drug and target sequence information allows the model to be widely applied to drugs and targets with unknown three-dimensional structures. 2.3 Toxicity prediction Deep learning is also employed to predict the potential toxicity of drug molecules, a crucial step in the drug development process. By analyzing the relationship between chemical structures and known toxicities, deep learning models can forecast potential toxicity issues of new molecules, aiding researchers to sidestep compounds likely to cause severe side effects at early stages. The Tox21 program, initiated by multiple federal agencies in the United States, aims to predict the toxicity of chemicals through high-throughput screening (HTS) techniques combined with deep learning models, reducing the need for traditional animal toxicity studies. According to Mayr et al. (2016), the DeepTox framework was developed to predict compound toxicity directly from molecular structures. In the Tox21 Challenge, DeepTox demonstrated superior performance. The DeepTox framework standardizes chemical characterizations of compounds, calculates a plethora of chemical descriptors as inputs for machine learning methods, trains models, and combines the best models into an ensemble to predict the toxicity of new compounds. In the Tox21 Data Challenge, the GGL-Tox model showcased its accuracy and efficiency in toxicity analysis and prediction by integrating geometric deep learning with gradient boosting decision tree algorithms (Jiang et al., 2021). Jimenez-Carretero et al. (2018) explored the potential of using deep convolutional neural networks (CNNs) to predict toxicity from pre-processed DAPI-stained cellular microscopy images of drugs. They found that the Tox-CNN model, based on nuclear profiling, outperformed other models in classifying cells by health status. This study validated the sensitivity and broad specificity of deep learning methods in predicting drug-induced toxicity from cellular images. 2.4 Drug design The application of deep learning in de novo drug design is particularly exciting. Using techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), researchers can design new molecular structures that theoretically possess high activity and low toxicity. These methods allow scientists to explore and generate novel molecules with specific biological activity features from a vast chemical space, significantly accelerating the drug discovery process. Kadurin et al. (2017) proposed an advanced autoencoder model, druGAN, for de novo generation of new molecular fingerprints with predefined anti-cancer properties. Compared to Variational Autoencoders (VAEs), druGAN offers advantages in the tunability of generated molecular fingerprints, capability in handling large molecular datasets, and efficiency in unsupervised pre-training for regression models.
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