**4.4 Possibility of drug design using topological data analysis**

If we have a dataset containing information about compounds with respect to their structural features, toxicity, binding affinity to the target then we can use TDA, a mathematical technique to create a similarity network of the compounds by studying shapes preserving high-dimensionality. TDA will enable us to visualize our compound library as a two-dimensional network, with compounds (located in nodes) linked by a series of edges indicating their degree of mutual similarity. As a result, two compounds with identical properties will appear closer together in the network, whereas two compounds with vastly different properties will appear farther apart. This network would make it simple to create subgroups or families of related compounds, which could then be used to choose the best compound.


#### **Table 2.**

*List of AI-Based Computational Tools for Drug Discovery.*

#### **4.5 Possibility drug design using Quantum machine learning**

The Quantum Computer Algorithm (QC) was introduced as a way to speed up classical machine learning algorithm. However, modern computers are not enough to analyze the behavior of the atoms in a molecule. Even the most powerful supercomputers at this time can only simulate relatively simple molecules by significantly limiting their ability to predict the interaction of complex molecules and atoms. Classical computing (CC) and big data analysis methods can screen desired properties of molecules. Researchers use Molecular simulator to simulate interactions between electrons of each atom to test how they react with each other in the real world. However, due to CC's relatively limited processing capacity, the computationally designed drug fails to deliver the desired results in 90 percent cases during the first phase of the clinical trial causing loss of billions of dollars every year. When Google announced a successful quantum computing experiment (QC) that has achieved what is called "quantum supremacy" (calculations that are too complex to do using CC), it was praised by many peoplearound the world as a key moment for QC.

*Drug Design for Malaria with Artificial Intelligence (AI) DOI: http://dx.doi.org/10.5772/intechopen.98695*

It is also an important development for drug discovery. Indeed, QC can change the drug discovery game entirely by packing enough computational muscles to enable molecular analysis at an unprecedented scale which was completely unimaginable with a CC. Additionally, quantum machine learning (QML) which uses quantum algorithms to perform complex algorithms of learning assignments [72] can augment the computational tools of drug discovery programs even further. Classical machine learning has already shown a significant promise in drug discovery. QML allows scientists to translate classical ML algorithms into quantum circuits to run an ultra-strong quantum computer efficiently. This quantum computer will become more efficient in future as the quantum technologies become less prone to mistakes. Scientists from the University of Warwick, the University of Luxembourg, and the University of Berlin recently created a deep ML algorithm that could predict the molecule's quantum state faster than before. "Solve the fundamental equation of quantum mechanics in conventional ways requires high-performance computing resources and computational months," explained the laboratory news. The team said that "the new AI algorithm developed can provide accurate predictions in seconds on a laptop or cellphone." Researchers from Pfizer have made similar progress, using modeling techniques called predictions of the crystal structure (CSP) to map molecular 3D structures - calculations that usually take months to solve. However, there is a long path to travel before we can say that we have achieved a practical quantum advantage. However, QC and QML hold extraordinary promises for the pharmaceutical business - which, when refined, will pave the way towards the development of new drugs as well as safer and cheaper health products for patients in the future.
