What is Machine Learning?

Machine learning (ML) refers to the study of computer algorithms to analyze data that automates analytical model building. ML is also defined as a subset of Artificial Intelligence (AI). ML algorithm builds model based on sample data, which is commonly known as “training data.” With the help of these data, predictions/decisions can be made without being explicitly programmed to do so. Developments/Advancements in computational science have accelerated drug designing and discovery. Artificial intelligence and machine learning technology play a significant role in drug discovery and development. Artificial intelligence in the healthcare and pharmaceutical industry has five significant applications, which has changed the entire scenario. These applications include research and discovery, clinical development, manufacturing and supply chain, patient surveillance, and post-market surveillance.

Why Choose Machine Learning?

Designing and development of drugs is an important field of research for pharmaceutical companies and chemical scientists. However, various factors such as off-target delivery, low efficacy, time consumption, and high cost develop many hurdles and challenges that impact drug designing and discovery. In addition, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline that has been a major concern. In other words, artificial neural networks and deep learning algorithms have modernized the area of research. Machine learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, ligand-based virtual screening, structure-based virtual screening, toxicity prediction, monitoring of the drugs and their release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. With advancements in science and technology, computer-aided drug design integrating machine learning algorithms can eliminate the challenges faced by traditional drug design and discovery.

In the past centuries, the world’s medical communities started allopathic approaches to treat and recover from the diseases. This change resulted in the success of fighting diseases, but high drug expenses became a healthcare burden. The ability of new analytics in data to synergize with classical approaches and prior hypotheses to create a novel hypotheses and models has proven itself useful in applications of repositioning, target discovery, small molecule discovery, synthesis etc.

Methods and Applications

In the past years of rational drug discovery, various machine intelligence approaches have been applied to guide the traditional experiments, which are expensive and time-consuming. ML methods classify various compounds and predict new active molecules. Over the past decades, machine-learning tools, like quantitative structure-activity relationship (QSAR) modeling, have been developed, which is capable of identifying potential biological active molecules from millions of candidate compounds cheaply and quickly.

Machine learning algorithms are used in fields such as medicine, computer vision, email filtering, and where it becomes difficult/unfeasible to develop conventional algorithms to perform the needed tasks. AI is widely used in both industries and academia. The basis of ML techniques requires a heavy mathematical followed by computational theory. Machine learning (ML) – an essential sub-component in AI that has been integrated into many fields, such as data generation and analytics. The discipline of ML includes various approaches to teach computers and accomplish tasks where no full satisfactory algorithm is available. ML techniques provide a set of tools that can improve the discovery of drugs and decision-making for well-specified questions with abundant, high-quality data.

ML algorithms can be applied in all stages of drug discovery that include target validation, find a new use of drugs, identification of prognostic biomarkers, prediction of drug-protein interactions, analysis of digital pathology data in clinical trials and optimization of the bioactivity of molecules. Machine learning can enhance many stages of the drug discovery process: preliminary but crucial stages, including designing a drug’s chemical structure, investigating the effect of a drug – both in basic, preclinical research and clinical trials, in which a lot of biomedical data is produced. Finding new patterns in those data can be facilitated by machine learning. The methods for drug designing targets and novel discovery of drugs now routinely combine machine learning and deep learning algorithms to enhance the efficacy, efficiency, and quality of the developed outputs. The incorporation and generation of big data using technologies such as high-throughput screening and computational analysis of databases utilized for both target and lead discovery has increased the reliability of machine learning and deep learning techniques.

Limitations

ML algorithm tools and methods have been an essential component of drug discovery. The algorithms increase efficiency and explore thousands of combinations that would have been impossible without the technology. The algorithms are trained with inputted data, but there lie several constraints with this algorithm. Although the biological pathway/targets being discovered through ML are found novel.

Information of the specific protein of interest might be limited, resulting in poor-extrapolated data. Data gathered from this method is generally utilized for training algorithms. However, not all the information comes from a wet lab; rather a computer-generated prediction is also utilized. The accuracy of the training data might show lower than that anticipated. A more concise way to understand is with the help of the statistical angle. With the prediction of algorithms, there was always a concern with underfitting/overfitting underfitting/overfitting.
Other limitations include:
• Requires large quantity of hand-crafted, structured training data.
• Learning must generally be supervised: Training data must be tagged.
• Systems remain opaque in nature which makes hard to debug.
• Cannot encode entities, or spatial relationships between entities.
• Can only handle very narrow aspects of natural language.
• Not suited for high-level, symbolic planning or reasoning.

Conclusion

Machine intelligence or machine learning – normally presented as artificial intelligence – is referred to the intelligence exhibited by computers. ML-based techniques attempt to revitalize the development and discovery of drugs. ML applications have paved a great way for algorithm-enhanced data query, analysis, and generation. Viable drug targets can be found using data clustering, regression, and classification from vast omics sources and databases, using ML techniques. AI algorithms have been carefully created and developed through several decades of research. This curation of function and utility to ML algorithms has shown continued success and development in drug discovery. With the help of precise algorithms, more powerful supercomputers, substantial public and private investment into the field, the ML applications will become more intelligent, time-efficient, cost-effective and during boost efficacy.