The search and design for the better use of bioactive compounds are used in many experiments to best mimic compounds' functions in the human body. However, finding a cost-effective and timesaving approach is a top priority in different disciplines. Nowadays, artificial intelligence (AI) and particularly deep learning (DL) methods are widely applied to improve the precision and accuracy of models used in the drug discovery process. DL approaches have been used to provide more opportunities for a faster, efficient, cost-effective, and reliable computer-aided drug discovery. Moreover, the increasing biomedical data volume in areas, like genome sequences, medical images, protein structures, etc., has made data mining algorithms very important in finding novel compounds that could be drugs, uncovering or repurposing drugs and improving the area of genetic markers-based personalized medicine. Furthermore, deep neural networks (DNNs) have been demonstrated to outperform other techniques such as random forests and SVMs for QSAR studies and ligand-based virtual screening. Despite this, in QSAR studies, the quality of different data sources and potential experimental errors has greatly affected the accuracy of QSAR predictions. Therefore, further researches are still needed to improve the accuracy, selectivity, and sensitivity of the DL approach in building the best models of drug discovery.

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深度学习的兴起和分子建模工具生物活性预测能力的转变

许多实验都在寻找和设计更好地利用生物活性化合物，以最好地模拟化合物在人体中的功能。然而，寻找一种经济高效且省时的方法是不同学科的重中之重。如今，人工智能 (AI) 尤其是深度学习 (DL) 方法被广泛应用于提高药物发现过程中使用的模型的精度和准确性。DL 方法已被用于为更快、高效、经济且可靠的计算机辅助药物发现提供更多机会。此外，基因组序列、医学图像、蛋白质结构等领域的生物医学数据量不断增加，使得数据挖掘算法在寻找可能成为药物的新化合物方面变得非常重要，发现或重新利用药物并改进基于遗传标记的个性化医疗领域。此外，深度神经网络 (DNN) 已被证明优于其他技术，例如用于 QSAR 研究和基于配体的虚拟筛选的随机森林和 SVM。尽管如此，在 QSAR 研究中，不同数据源的质量和潜在的实验误差极大地影响了 QSAR 预测的准确性。因此，仍需要进一步研究以提高 DL 方法在构建最佳药物发现模型方面的准确性、选择性和灵敏度。在 QSAR 研究中，不同数据源的质量和潜在的实验误差极大地影响了 QSAR 预测的准确性。因此，仍需要进一步研究以提高 DL 方法在构建最佳药物发现模型方面的准确性、选择性和灵敏度。在 QSAR 研究中，不同数据源的质量和潜在的实验误差极大地影响了 QSAR 预测的准确性。因此，仍需要进一步研究以提高 DL 方法在构建最佳药物发现模型方面的准确性、选择性和灵敏度。