In silico design of bioisosteric modifications of drugs for the treatment of diabetes

Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work?

Utilizing artificial intelligence (AI) in drug design represents an advanced approach for identifying targets and developing new drugs. Integrating AI techniques significantly reduces the workload involved in drug development and enhances the efficiency of early-stage drug discovery. This review aims to present a comprehensive overview of the utilization of AI methods in the field of small drug design, with a specific focus on four key areas: protein structure prediction, molecular virtual screening, molecular design, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Additionally, the role and limitations of AI in drug development are explored, and the impact of AI on decision-making processes is studied. It is important to note that while AI can bring numerous benefits to the early stage of drug development, the direction and quality of decision-making should still be emphasized, as AI should be considered as a tool rather than a decisive factor.

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

Thiazolidinediones: An In-Depth Study of Their Synthesis and Application to Medicinal Chemistry in the Treatment of Diabetes Mellitus

2,4-Thiazolidinedione (TZD) is a privileged and highly utilised scaffold for the development of pharmaceutically active compounds. This sulfur-containing heterocycle is a versatile pharmacophore that confers a diverse range of pharmacological activities. TZD has been shown to exhibit biological action towards a vast range of targets interesting to medicinal chemists. In this review, we attempt to provide insight into both the historical conventional and the use of novel methodologies to synthesise the TZD core framework. Further to this, synthetic procedures utilised to substitute the TZD molecule at the activated methylene C5 and N3 position are reviewed. Finally, research into developing clinical agents, which act as modulators of peroxisome proliferator-activated receptors gamma (PPARγ), protein tyrosine phosphatase 1B (PTP1B) and aldose reductase 2 (ALR2), are discussed. These are the three most targeted receptors for the treatment of diabetes mellitus (DM).