Over the past few decades, the life expectancy of humankind has increased significantly due to advancements in life sciences and medical research, particularly given our increasing success in the ...epidemiological and pharmacological management of bacterial, fungi, and viral infections ....
Machine learning and artificial intelligence (ML/AI) have become important research tools in molecular medicine and chemistry. Their rise and recent success in drug discovery promises a rapid ...progression of development pipelines while reshaping how fundamental and clinical research is conducted. By taking advantage of the ever-growing wealth of publicly available and proprietary data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses. Hitherto unknown data patterns may guide and prioritize experiments, and augment expert intuition. Therefore, data is a key component in the model building workflow. Herein, I aim to discuss types of chemical and biological data according to their quality and reemphasize general recommendations for their use in ML/AI.
With the expected explosive growth of the number of mobile user devices and data traffic in the next decade and beyond, and the expected development of various new applications and services such as ...vehicle-to-everything, extended reality and smart city, the requirements for Quality of Service (such as ultra-high data transmission rate, ultra-low latency, support for the highest moving speed and seamless connection, etc.) will reach levels never seen before. The sixth generation (6G) of networks is conceived to address the requirements mentioned above. Digital Twin refers to a digital representation to represent a physical entity in virtual space, while its corresponding physical object remains in the physical space. In this article, we first briefly introduce some basic knowledge of Digital Twins. We then outline possible innovative technologies expected in 6G networks that will help Digital Twin systems to reach full potential, and deliver the high-level performance needed for the next generation of communications.
Additive manufacturing has revolutionized the manufacturing paradigm in recent years due to the possibility of creating complex shaped three-dimensional parts which can be difficult or impossible to ...obtain by conventional manufacturing processes. Among the different additive manufacturing techniques, wire and arc additive manufacturing (WAAM) is suitable to produce large metallic parts owing to the high deposition rates achieved, which are significantly larger than powder-bed techniques, for example. The interest in WAAM is steadily increasing, and consequently, significant research efforts are underway. This review paper aims to provide an overview of the most significant achievements in WAAM, highlighting process developments and variants to control the microstructure, mechanical properties, and defect generation in the as-built parts; the most relevant engineering materials used; the main deposition strategies adopted to minimize residual stresses and the effect of post-processing heat treatments to improve the mechanical properties of the parts. An important aspect that still hinders this technology is certification and nondestructive testing of the parts, and this is discussed. Finally, a general perspective of future advancements is presented.
Synthetic organic chemistry underpins several areas of chemistry, including drug discovery, chemical biology, materials science and engineering. However, the execution of complex chemical syntheses ...in itself requires expert knowledge, usually acquired over many years of study and hands-on laboratory practice. The development of technologies with potential to streamline and automate chemical synthesis is a half-century-old endeavour yet to be fulfilled. Renewed interest in artificial intelligence (AI), driven by improved computing power, data availability and algorithms, is overturning the limited success previously obtained. In this Review, we discuss the recent impact of AI on different tasks of synthetic chemistry and dissect selected examples from the literature. By examining the underlying concepts, we aim to demystify AI for bench chemists in order that they may embrace it as a tool rather than fear it as a competitor, spur future research by pinpointing the gaps in knowledge and delineate how chemical AI will run in the era of digital chemistry.Artificial intelligence has recently seen numerous applications in synthetic organic chemistry. Advanced pattern-recognition heuristics may facilitate the access to chemical matter of interest and complement chemical intuition in the near future.
De novo molecular design and in silico prediction of polypharmacological profiles are emerging research topics that will profoundly affect the future of drug discovery and chemical biology. The goal ...is to identify the macromolecular targets of new chemical agents. Although several computational tools for predicting such targets are publicly available, none of these methods was explicitly designed to predict target engagement by de novo-designed molecules. Here we present the development and practical application of a unique technique, self-organizing map—based prediction of drug equivalence relationships (SPiDER), that merges the concepts of self-organizing maps, consensus scoring, and statistical analysis to successfully identify targets for both known drugs and computer-generated molecular scaffolds. We discovered a potential off-target liability of fenofibraterelated compounds, and in a comprehensive prospective application, we identified a multitarget-modulating profile of de novo designed molecules. These results demonstrate that SPiDER may be used to identify innovative compounds in chemical biology and in the early stages of drug discovery, and help investigate the potential side effects of drugs and their repurposing options.
Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical ...entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.
Representation learning is making inroads into drug discovery. A study in Nature Communications emphasizes multiple limitations in property prediction. The results suggest that continued research and ...improvements are required for this specific area that coalesces machine learning and molecular medicine.Machine learning is a powerful tool for the study and design of molecules. Here the authors comment a recent publication in Nature Communications which highlights the challenges of different molecular representations for data-driven property predictions.
Ultracold-Wire and arc additive manufacturing (UC-WAAM) Rodrigues, Tiago A.; Duarte, Valdemar R.; Miranda, R.M. ...
Journal of materials processing technology,
October 2021, 2021-10-00, 20211001, Letnik:
296
Journal Article
Recenzirano
Display omitted
•A novel WAAM variant, Ultracold-wire and arc additive manufacturing (UC-WAAM), was developed.•In UC-WAAM the electric arc is established between a non-consumable tungsten electrode ...and the wire feedstock.•Decreased thermal impact on the UC-WAAM-based parts, without loss of productivity.•High strength and ductility of the as-built parts were preserved.•An overhang structure was successfully produced with UC-WAAM.
This study presents a new variant of Wire and Arc Additive Manufacturing named Ultracold-Wire and Arc Additive Manufacturing (UC-WAAM), in which the electric arc is established between the wire feedstock material and a non-consumable tungsten electrode. A feasibility study was performed on HSLA steel. A comparison with conventional Gas Metal Arc Welding-based WAAM was performed. By removing the electric current through the substrate and distancing the electric arc from the molten pool, this new WAAM variant can reduce the process temperatures and increase cooling rates, without compromising the integrity or deposition rate of the as-built parts. Mechanical testing showed the preservation of the high mechanical strength and ductility of the HSLA steel parts built with UC-WAAM. Lastly, one of the unique features of this new variant is also presented: the use of a non-conductive/non-metallic block to produce overhang structures, highlighting the potential for UC-WAAM to be used to fabricate supportless complex-shaped 3D structures.