Artificial intelligence in drug discovery and development

Abstract

Highlights Artificial Intelligence (AI) has revolutionized many aspects of the pharmaceuticals. AI assistance to pharma industries helps to improve overall life cycle of product. AI can be implemented in pharma ranging from drug discovery to product management. Future challenges related to AI and their respective solutions have been expounded.

Challenges and advances in clinical applications of mesenchymal stromal cells

“…To understand the essential elements in MSCs treatment, AI may recognize the dynamic molecular characteristics of essential elements, which include different protein sequences, molecular structures, as well as the binding forces and stabilities between targeted molecules and cell receptors. These data could be used to train a predictive model to the utmost accuracy [ 187 ]. Predicted elements may also be produced under AI guidance.…”

The Integration of Artificial Intelligence in Drug Discovery and Development

“…In contrast to the conventional approach, AI, particularly ML, uses virtual screening of big data to predict therapeutic targets and identify suitable drug candidates for the disease variant [13]. ML is capable of analysing vast amounts of information from areas such as gene mapping, pharmacokinetics, solubility profiles and receptor affinities to predict properties of novel agents with their target counterparts [4]. Meanwhile, DL is evolving in the discovery of de novo compounds by proposing new synthesis routes of previously discovered molecules and creating molecules not previously synthesised ever before [14].…”

“…Further understanding of drug efficacy, administration routes, pharmacokinetics/pharmacodynamics and interactions with polypharmacy are obtained. The use of AI transforms the preclinical study period by eliminating the approach of a 'trial and error' methodology [4]. Instead, it replaces it with ML predictions of; drug formulation and manufacturing, therapeutic doses, administration routes with the highest efficacy and suitable storage methods.…”

“…AI is comprised of several different domains; one of which is Machine Learning (ML). ML equips AI technology by using algorithm-based methods to learn from experience and recognise patterns within data to make predictions and decisions [4]. It allows the system to learn, adapt and autonomously improve accuracy overtime.…”

“…Furthermore, a subset of ML is Deep Learning (DL), whereby data is organised in artificial neural networks, mirroring risk-based stratification used by the human brain [5]. Together, ML and DL contribute to improve interpretation of abundant, complex, high quality data to mimic human intelligence in decision making [4]. However, the limitations of data acquisition, application and prediction of future events for complex individualised problems remain a challenge.…”