The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. The primary concern associated with drug design and development is time consumption and production cost. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. In other words, artificial neural networks and deep learning algorithms have modernized the area. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Google Translate uses deep learning techniques to translate based on the semantics of an entire sentence instead of just memorizing phrase-to-phrase translations.Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. Natural Language Processing – Modern deep learning techniques have led to improvements in translation and language modeling.Computer Vision – Images are used to train the machine to recognize features and now the machines are demonstrating “superhuman” accuracy for image recognition.Automatic Speech Recognition – All major commercial speech recognition systems (think your smart phone assistant) use a deep learning technique with recurrent neural networks currently being the most popular.Because the machine is also learning from the processed data, it is able to perform feature extraction and abstraction automatically from the raw data with little to no human input. As the amount of data increases, the machine becomes more adept at recognizing even hidden patterns among the data. ![]() This learning technique is a groundbreaking tool for processing large quantities of data, since the performance of the machine improves as it analyzes more data.
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