Scaffold-Directed Face Selectivity Machine-Learned from Vectors of Non-covalent Interactions

This work describes a method to vectorize and Machine-Learn, ML, non-covalent interactions responsible for scaffold-directed reactions important in synthetic chemistry. Models trained on this representation predict correct face of approach in ca. 90 % of Michael additions or Diels-Alder cycloadditions. These accuracies are significantly higher than those based on traditional ML descriptors, energetic calculations, or intuition of experienced synthetic chemists. Our results also emphasize the importance of ML models being provided with relevant mechanistic knowledge; without such knowledge, these models cannot easily "transfer-learn" and extrapolate to previously unseen reaction mechanisms

Application of ultra-high dose rate (FLASH) proton beam for the 3D cancer cell model : a proof of concept

Ultra-high dose rate (FLASH) proton radiotherapy is a promising treatment method for cancer patients. In our research, we want to compare the FLASH method with a conventional radiation method to show what effect they have on the biochemical structure of the tumour (3D model - spheroids) and the secretion of EVs and their cargo. The use of a modern method of creating spheroids will enable us to create conditions that are more able to mimic the tumour microenvironment

Computer-Assisted Planning of Hydroxychloroquine’s Syntheses Commencing from Inexpensive Substrates and Bypassing Patented Routes.

A computer program for retrosynthetic planning helps develop multiple "synthetic contingency" plans for hydroxychloroquine, a promising but yet unproven medication against COVID-19. These plans are designed to navigate, as much as possible, around known and patented routes and to commence from inexpensive and diverse starting materials, such as to ensure supply in case of anticipated market shortages of the commonly used substrates

Motor Evoked Potentials after Supraspinal Stimulation in Pre- and Postoperative Evaluations of Patients with Cervical Radiculopathy

Objective. Pre- and postoperative comparative evaluation of neurophysiological tests and clinical trials. Analysis of the diagnostic value of motor evoked potentials (MEP) induced by a magnetic field after supraspinal stimulation. Evaluation of the sensitivity and specificity of electromyography (EMG) and MEP is achieved. Methods. EMG, ENG, M-wave, F-wave, and MEP tests were performed on 35 patients with confirmed cervical radiculopathy in pre- and postoperative evaluations. The clinical trial consisted of evaluation of muscle strength, a sensory perception test and evaluation of tendon reflexes and pain severity. Results. The sensitivity of the resting EMG and MEP tests is 24%-67% and 6%-27%, while their specificity is 43%-80% and 86%-100%, respectively. The postoperative evaluation revealed a statistically significant reduction in pain severity (p=0001), an increase in muscle strength in DP (p=0.0431), BB (p=0,0431), and TB (p=0.0272), and improvement of touch sensation in terms of dermatomal innervation in C5 (p=0.0001) and C6 (p=0.0044). Conclusions. Tests comparing MRI sensitivity to neurophysiological tests show that neuroimaging is more sensitive in diagnostics of patients with cervical radiculopathy; however, clinical neurophysiology tests are more specific in reference to clinical trials

Suggestions for second-pass anti-COVID-19 drugs based on the Artificial Intelligence measures of molecular similarity, shape and pharmacophore distribution.

Artificial Intelligence algorithms are used to identify “progeny” drugs that are similar to the “parents” already being tested against COVID-19. These algorithms assess similarity not only by the molecular make-up of the molecules, but also by the “context” in which specific functional groups are arrangedand/or by three-dimensional distribution of pharmacophores. The parent-progeny relationships span same-indication drugs (mostly antivirals) as well as those in which the “progenies” have different and perhaps less intuitive primary indications (e.g., immunosuppressant or anti-cancer progenies from antiviral parents). The “progenies” are either already approved drugs or medications in advanced clinical trials – should the currently tested “parent” medicines fail in clinical trials, these “progenies” could be, therefore, re-purposed against the COVID-19 on the timescales relevant to the current pandemic.</div

A computer algorithm to discover iterative sequences of organic reactions

Iterative syntheses comprise sequences of organic reactions in which the substrate molecules grow with each iteration and the functional groups, which enable the growth step, are regenerated to allow sustained cycling. Typically, iterative sequences can be automated, for example, as in the transformative examples of the robotized syntheses of peptides, oligonucleotides, polysaccharides and even some natural products. However, iterations are not easy to identify???in particular, for sequences with cycles more complex than protection and deprotection steps. Indeed, the number of catalogued examples is in the tens to maybe a hundred. Here, a computer algorithm using a comprehensive knowledge base of individual reactions constructs and evaluates myriads of putative, but chemically plausible, sequences and discovers an unprecedented number of iterative sequences. Some of these iterations are validated by experiment and result in the synthesis of motifs commonly found in natural products. This computer-driven discovery expands the pool of iterative sequences that may be automated in the future