AI is not new to healthcare or associated researches. Scientists and researchers have long been putting efforts into utilizing AI capabilities in bringing breakthroughs in healthcare on a broader level. 

This is an appropriate time to utilize AI in bringing speed, strength, and faith to the medical solutions, which the world awaits right now. It wouldn’t be wrong if we say these are the exact times when AI will prove to be worthy enough in pharmaceuticals and healthcare too.

COVID-19 is a WHO-declared pandemic that has globally affected around 152,534,452 cases so far. India has so far witnessed around 19,925,604 cases and approximately 218,959 deaths.

As of 1 May 2021, a total of 1,045,850,203 vaccine doses have been administered globally.

These alarmingly rising numbers led to the world seeking quick remedial responses from the pharmaceutical industry. The need for a credible and effective vaccine to deal with the COVID-19 crisis. With such drastic numbers, stubborn virus, and lack of time, research for the vaccine couldn’t afford to be conventional and slow. The role of AI in drug discovery and research has been promising, and even in the current circumstances, AI-based models are revolutionizing the associated medicine and vaccine discovery.

The use of conventional drug discoveries had issues such as lengthy timelines and hence high associated costs.

With deep learning, researchers can extract features from raw data. ML has a significant contribution in areas of drugs and vaccine discoveries. With large volumes of data and automatic abstract feature learning, ML is set to make a mark in various application areas. AI and ML are involved in creating models that recognize patterns and learn from the available data to draw inferences. These inferences can also be drawn from previously available data.

ML helps in the following areas specific to drug and vaccine discovery.

• Compound property prediction
• Activity prediction
• Reaction prediction
• Ligand-protein interaction

The automatic feature extraction capabilities of deep learning can support models with better accuracy to provide more reliable results. The generative capabilities of deep learning can be utilized to produce more draggable molecules that could lower the chances of failure during the trials. 

There is an issue of scarcity of knowledge and data around these novel viruses, which requires transfer learning and utilizing the previously acquired knowledge or working on the big data. Hence, the adoption of deep learning in therapy discovery for SARS-COV-2 is essential in order to make a timely and accurate response to the virus.

Graph Convolutional Neural Networks (GCNN) are important tools in drug discovery applications. They help in a clear understanding of molecules and, in turn, assist in drug property prediction, reactivity prediction, Durg-target interactions, and more.

Deep learning is actively being used for lead generation. In this approach, deep learning models such as Generative Adversarial Networks (GANs) help to create data-oriented molecules. Deep learning generative models are working to develop sequences of atom for medicine research. This idea resolves the constraining issues of ligand-based designs with improved diversity.

The implementation of Reverse Vaccinology (RV) along with ML has shown promising results for antigen predictionin the past. Using a web-based RV program combined with ML-based approaches, scientists are bacterial antigen production has achieved accuracy and refinement. 

These advancements have been the essence of feature extraction, feature selection, data augmentation, and cross-validation, which are important to predict vaccine candidates against various bacterial and viral pathogens known to cause infectious disease.

With deep learning’s predictive capabilities, today, even cancer vaccinology has advanced itself.

In drug development cases, it is critical that the resulting drug is safe for human consumption and has minimal minor side effects. This definitely requires thorough observation and references. The Toxicology in the 21st Century program (Tox-21) has developed a database of 10,000 compounds from 70 screening assays to facilitate toxicity modeling.This is where AI can exhibit its potential in deducing important learnings and patterns from the database to produce more accurate results in much lesser time.

The usage of AI has been distinguished in various sectors, including proper utilization of AI in the discovery of potential candidates, while others may include clinical trials on patients in order to explore the efficacy and proper combination of drugs or dosage in designing potential therapy. 

Narrowing down to the right drug combination therapy using AI will prove important in drug research for Coronavirus disease (COVID-19). The pandemic undoubtedly needs fast intervention and effective regimes.

Computational AI and molecular chemistry are together creating meaningful drug design methods for COVID-19.Developments in Artificial Intelligence and Computational Drug Designing techniques are becoming the benchmark for the COVID-19, opening new avenues for drug discovery.

There is a dire need for new approaches as the cost of drug development is increasing with decreasing investment returns. In the current crisis, the solution lies in AI intervention for immediate SARS-COV-2 therapy discovery. The world has faced various deadly viruses and has beaten them and learned from them. Today, we use the same ideology of learning from the past but with the strong support of technology to become stronger and better equipped.

Image by hakan german from Pixabay