**5. Conclusions**

Many pharmaceutical companies are using AI tools in drug discovery process. Costs and time remain big challenges in drug discovery programs. There are usually about 1 million compounds in a standard high-throughput screening library, where designing each compound typically costs 50–100 USD. As a result, an initial screening phase would cost several million dollars and take months to complete. AI or ML techniques help to do optimization of lead compound. It takes only few days to find the lead compounds by AI, when classical approach takes several years. AI helps to predict bio-activity, toxicity, physical properties, structure prediction of potential drug. There are few companies like Merck, Novartis etc. who are using AI technologies to design drug. Classical computer has some limiting computing power. So, Researcher are trying to make quantum computer or using quantum machine learning algorithm in Classical computer to do computation in a faster way. Since, malaria is one of the major health burdens in the developing world, AI based drug design programs will be immensely helpful in aiding WHO's goal to reduce cases of malaria by 90 percent by 2030. The inefficacy of vaccination strategies further impose all the burdens on continuous discovery of new drugs. We strongly suggest through this review that AI based drug program would substantially benefit in tackling this debilitating disease with respect to saving human life at lower amount of time and cost.

#### **Acknowledgements**

Authors thank Department of Biotechnology (No. BT/RLF/Re-entry/32/2017), Government of India for funding this project.

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