The protein kinase family is considered to be a promising target for Drug Discovery, as it plays a key role in controlling signal transduction in the cell. Abnormally elevated expression levels and mutations of protein kinase genes lead to disruptions in the cell signaling network, which is associated with pathological conditions such as inflammation and cancer.
Before we can dive into statistical learning, we must first create a solid foundation. The article "Best practices in Machine Learning in Chemistry" presents a collection of best practice standards and a matching checklist that can be used as a compass for navigating the challenging field of chemical machine learning. The researchers' goal was to maximize the validity and reproducibility of the conclusions and models derived from these best practices.
The amount of work done by humans was greatly decreased by the Industrial Revolution and the related mechanization. To lessen workloads in a manner like to that of the Industrial Revolution, numerous researchers are attempting to incorporate Artificial Intelligence (AI) and Machine Learning (ML) into various intellectual job processes.
Chemistry has experienced a fundamental shift toward the use of statistical modeling and analysis in the constantly changing world of scientific research. These technologies have proven to be priceless resources that go beyond the limitations of human intuition and help researchers discover brand-new, elusive links. Chemspace offers a comprehensive service that combines DNA-encoded libraries (DEL), machine-learning models, and large chemical spaces to enhance early Drug Discovery. This approach, initially introduced by Kevin McCloskey et al., has shown promising results in obtaining active compounds for screening. Chemspace’s full-service approach includes DEL screening, machine-learning model development, and the provision of potentially active compounds, making it ideal for projects with limited structural or activity data.
In today’s world, the advent of certain technologies has made problem-solving easier. For instance, computational chemistry has enabled rapid resolution of chemical issues, while mathematics and computer tasks have been streamlined. Chemists can simulate experimental outcomes and determine material properties. Additionally, there’s a growing integration of Machine Learning (ML) concepts and algorithms in various fields.
In today’s fast-paced world, technology permeates every aspect of our lives. From the way we live, work, and play, to the revolutionary advancements in various fields, one particular area that stands out is Artificial Intelligence (AI) and its close companion, Machine Learning (ML). These cutting-edge technologies have brought about significant changes and improvements across multiple industries, including the scientific community. How much do people know about these unique aforementioned developments?
This scientific article explores the potential of artificial intelligence (AI) to mitigate and prevent future pandemics. Through a comprehensive analysis of current AI applications in the field of health, the unique capabilities of this technology to address pandemic challenges are highlighted. AI can improve early disease detection through the analysis of large volumes of data, enabling more effective epidemiological surveillance. Additionally, AI models can predict disease spread and assist experts in making informed decisions regarding control measures. AI also plays a crucial role in the development of vaccines and drugs, accelerating the discovery and optimization process. Furthermore, AI can support remote healthcare by facilitating telemedicine and real-time patient monitoring. While there are ethical and privacy challenges associated with the use of AI, it is evident that this technology can play a fundamental role in preparing for and responding to future pandemics, significantly improving global health and societal well-being.
This study examined the effect of cellulose nanocrystal (CNC) loading on curing characteristics and mechanical properties of acrylonitrile-butadiene rubber (NBR)/natural rubber (NR) nanocomposites. The blend ratio of NBR and NR was kept constant at 50/50, excluding the raw NBR (NBR100). Four distinct samples with different CNC loadings, in parts per hundred rubber (phr), including NBR-C0 (0 phr), NBR-C1 (1 phr), NBRC-3 (3 phr), and NBRC-5 (5 phr), were prepared using an internal mixer at 70 oC and a rotor speed of 15 rpm. The cure characteristics of the nanocomposites were studied using a moving die rheometer (MDR), while the mechanical properties of the vulcanizates were measured in accordance with ASTM standards. The results showed that scorch time, cure time and delta torque of rubber composites tended to increase with the CNC content, while NBR100 showed higher delta torque than those properties. Moreover, hardness, as well as the 100% and 300% modulus of rubber vulcanizates slightly increased with the CNC content. Furthermore, a NBR50-C1 composite containing CNCs at 1 phr showed an optimal value for tensile strength.