Additionally, drug discovery and designing comprise long and complex steps such as target selection and validation, therapeutic screening and lead compound optimization, pre-clinical and clinical trials, and manufacturing practices. Artificial intelligence (AI), including deep learning (DL) and machine learning (ML) algorithms, has emerged as a possible solution, which can overcome problems and hurdles in the drug design and discovery process. Thus, there is a surge in the implementation of novel techniques, which are self-sufficient to eliminate the challenges encountered in traditional computational approaches. However, these techniques also impose challenges such as inaccuracy and inefficiency. To minimize these challenges and hurdles, researchers around the globe moved toward computational approaches such as virtual screening (VS) and molecular docking, which are also known as traditional approaches. Further, the cost of development and time consumption in developing novel therapeutic agents was another setback in the drug design and development process. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.įrom the past two decades, the development of efficient and advanced systems for the targeted delivery of therapeutic agents with maximum efficiency and minimum risks has imposed a great challenge among chemical and biological scientists. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. 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. Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists.
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