Research Paper on AI in Pharma: Unlocking Scientific Breakthroughs Through Machine Learning

Introduction

The pioneers of Artificial Intelligence (AI) started designing building machines in the 1950s. This technology facilitates the analysis of datasets through the application of advanced algorithms. The recent progress in machine learning has enhanced research in the pharmaceutical industry and consequently scientific breakthroughs. Currently, researchers use Artificial Intelligence to identify, predict and analyze critical patterns underlying large volumes of data. However, the pharmaceutical industry is facing challenges in developing drugs and hence the need to incorporate AI in their scientific analysis. The inability to sustain drug development programs, in particular, is a critical factor driving the pharmaceutical companies to use AI.

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This topic is essential since it improves the understanding of how AI is revolutionizing drug discovery in the pharmaceutical industry. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are the most recent developments in the applications of AI in molecular drug design. The review of the literature at this point is essential as it points out how AI is improving efficiency in drug discovery and its ethical issues. The study of robotics systems in drug discovery apply the concepts of the atomistic theory. Alexander Crum Brown, the founder of this model, argued that objects consist of multiple indestructible and indivisible simple elements (Sellwood et al., 2018).

Chemists have attempted to predict the fundamental properties of compounds without necessarily synthesizing them. In the early 1960s, researchers introduced Quantitative Structure-activity Relationship Models (QSAR) to determine the representations of molecules. The use of QSAR is a breakthrough that established the basis for the development of AI in the 1960s. With access to significant structure data, researchers saw a need to generate predictive models, and consequently AI. The outcome is the applications of AI in synthesizing compounds and identifying their chemical structures. The two aspects are critical in the pharmaceutical industry since they are the basis of drug development and discovery.

Literature Review

The role of automated systems in the medical care industry has attracted voluminous research. Pharmaceutical companies are making efforts to embrace AI for inevitable reasons. This technology is beneficial since it enhances drug discovery processes and hence the generation of superior medicines. Also, its applications diminish the chances of failure rates in clinical trials besides its role in reducing research and development costs (Mak & Pichika, 2019). The pharmaceutical companies use AI to implement personalized medicines in their research. This strategy involves evaluating enormous data in clinical trials to determine the best cure options. Cloud-based systems, for instance, is a critical application of AI that facilitates the analysis of patterns in the curative efficiency of drugs during the development processes.

Drug-research companies in collaboration with software firms have developed critical AI technologies to facilitate the extensive processes of discovering therapy. The objective, in this case, is to reduce the costs and the time to come up with new drugs. Agrawal (2018) cites the recent collaboration between Microsoft and Bloomberg Technology in assisting the pharmaceutical firms in their efforts to discover cancer-curing drugs. The two companies have designed the "Hanover," artificial intelligence that can memorize and systemize information necessary for pharmaceutical companies to come up with medicines for curing cancer. The objective of this AI is to amalgamate drugs and recommend the best combination that is efficacious in the treatment of cancer. The author further cites the development of another algorithm based AI with high accuracy at Stanford University. This artificial intelligence has the same capability as that of a professional doctor in identifying skin cancer. It has positive contributions to the drug discovery efforts in that it recommends to researchers drug amalgamation that improve treatment efficiency.

Artificial Intelligence has contributed to the present-day scientific discovery of drugs. It has a high capability to identify hidden molecular patterns in biomedical data. The outcome is scientific breakthroughs in the development of vaccines and antibiotics. Nonetheless, the process of introducing new drugs to the market take decades with significant cost implications. The present-day research organizations, therefore, are turning to AI as a strategy to address high costs and long duration to discover drugs. Such pharmaceutical firms use a variety of AI tools to enhance their experimental studies. In this regard, AI has mechanisms that facilitate the invention of new drugs. The robotic system selects relevant information necessary to design therapies. Also, it can model and classify data, execute regression, prediction, and optimization.

The discovery of drugs involves analysis of molecular processes where the researchers may have limited understanding. With artificial intelligence, researchers can use predictive models to determine and analyze complex relationships underlying molecular descriptors. Therefore, AI improves the knowledge of the biological activities of molecular compounds in experimental research. In the realm of drug discovery processes, artificial intelligence facilitates research through its predictive models that classify compounds based on their activity and chemical properties. The modern pharmaceutical companies employ AI with high predictive models. Such models have a high capability to approximate the strength of the binding affinities in different substrates. Researchers use AI predictors in silico screening, a robotic system that has a high capacity to identify a drug substance with desired properties.

The accuracy of artificial intelligence in identifying tested outcomes and their correlation with informative biomarkers has critical contributions to drug research. Artificial Neural Network (ANN) is an example of a pharmaceutical modeling technique that solves complex problems in drug research. (Manallack & Livingstone, 2004). This application has a high capability to detect complex non-linear relationships in interactions between different compounds. The technique incorporates both experimental data and literature based evidence to determine molecular combinations that give the best set of drug compounds. Agatonovic-Kustrin and Beresford (2000) observed that ANN is transforming pharmaceutical discoveries through its strengths such as modeling, prediction, classification and pattern recognition.

However, the drug discovery process involves multivariate optimization problems. It suggests that the researchers have to optimize process variables and formulation to come up with new pharmaceutical products. While it is a challenge to model such a relationship using classical models, researchers use AI to determine an optimum formulation. This robotic system designs the relationship between individual pharmaceutical responses and causal factors that is necessary to discover a therapy with high efficiency. The use of ANNs as one of the AI support pharmaceutical researchers when evaluating complex multivariate non-linear correlation. One of the essential features of ANNs is its ability to generalize and predict the outputs from a set of data. This property is what the pharmaceutical firms use to solve formulation and optimization problems that aid the development of a new product.

With the existence of sophisticated artificial intelligence, pharmaceutical firms can quickly discover multiple drugs with a short time with low-cost implications. According to Williams et al., (2015), the discovery of a new drug takes more than $1 billion and lasts for more than ten years. This situation has increased the prevalence of tropical diseases such as Chagas, schistosomiasis, and malaria. Artificial intelligence has changed this scenario by reducing research costs and time of conducting clinical trials. The authors noted that the introduction of the robotic system has improved drug discovery for diseases which researchers had neglected for a long time because of their economic reasons. Williams et al., (2015) studied the effectiveness of "Eve," a laboratory information system that uses AI. The findings suggest that the automation system facilitates the discovery of new knowledge through experimentation research.

Laboratory automation systems using AI is the basis of discovering scientific knowledge. The implementation of such systems has ultimately led to the discovery of pharmaceutical products. It can potentially select research compounds economically and thus facilitating scientific research. However, several factors determine the economics of drug discovery. Difficulty in achieving intervention and safety standards are critical factors. The automation of the underlying processes, therefore, makes an economic difference in therapy discovery. AI is crucial during the final stages of experimentations since it takes into accounts standardization, solubility, and safety among other essential aspects. While human biology is a complex process, AI is at the center of drug discovery since its suggestions dramatically reduce the drug failure rate. Robotics systems can potentially confirm compounds, standardize QSAR cycles and guide researchers on optimum formulation and chemical synthesis necessary to design to a new pharmaceutical product.

Property prediction is an essential feature of AI that contributes to pharmaceutical breakthroughs. Researchers come up with new pharmaceutical products when clinical candidate molecules in the experimentation research can meet a set of different criteria. Automation systems using artificial intelligence, support discovery of drugs by selecting compounds that meet biological targets. Also, it enables pharmaceutical researchers to identify compounds that have good physicochemical and ADMET properties (absorption, distribution, metabolism, excretion, and toxicity). By selecting compounds that exhibit desirable compound features, researchers are assured of efficient compound design. Several silico-prediction methods of AI reduce the chances of errors and hence the effectiveness of a drug substance. Examples of learning machine technologies that use artificial intelligence in the pharmaceutical industry are Random Forests (RF), Bayesian learning, and Super Vector Machines (SVM) (Rogers, Brown & Hahn, 2005; Svetnik et al., 2003). These robotics systems according to the authors can revolutionize pharmaceutical research through property prediction.

Pharmaceutical firms use AI for de novo design, which refers to the generation of active molecules (Hartenfeller & Schneider, 2011; Schneider & Schneider, 2016). With artificial intelligence, researchers can generate new non-existing molecules that are required for drug discovery. Over the last two decades, it was synthetically challenging to come up with compounds that are an integral part of scientific breakthrough. One of its applications is searching an optimal solution using in-silicon model. A study by Ertl and Schuffenhauer (2009) shows that AI has synthetic accessibility scores (SAS) and QED drug-likeness scores to predict a path of molecules that design an effective pharmaceutical product.

Artificial intelligence enhances organic synthesis planning, a vital aspect of the molecule therapy discovery program. AI synthesize new molecules through compound optimization path and consequently determine molecular compounds with improved properties. In some instances, the challenges in synthesis planning limit the availability of chemical space. This aspect s...