Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning.

Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development.

Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science.

Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity.

Closed-loop transfer enables artificial intelligence to yield chemical knowledge.

Artificial intelligence-guided closed-loop experimentation has emerged as a promising method for optimization of objective functions, but the substantial potential of this traditionally black-box approach to uncovering new chemical knowledge has remained largely untapped. Here we report the integration of closed-loop experiments with physics-based feature selection and supervised learning, denoted as closed-loop transfer (CLT), to yield chemical insights in parallel with optimization of objective functions. CLT was used to examine the factors dictating the photostability in solution of light-harvesting donor-acceptor molecules used in a variety of organic electronics applications, and showed fundamental insights including the importance of high-energy regions of the triplet state manifold.

Outcomes and atrial substrate analysis in patients with HIV undergoing atrial fibrillation ablation.

Introduction: Patients with HIV infection have increased risk of atrial fibrillation, but the pathophysiologic mechanisms and the utility of catheter ablation in this population are not well-studied. We aimed to characterize outcomes of atrial fibrillation ablation and left atrial substrate in patients with HIV.

Methods: The study was a retrospective propensity score-matched analysis of patients with and without HIV undergoing atrial fibrillation ablation.

Coulomb Drag between a Carbon Nanotube and Monolayer Graphene.

We have measured Coulomb drag between an individual single-walled carbon nanotube (SWNT) as a one-dimensional (1D) conductor and the two-dimensional (2D) conductor monolayer graphene, separated by a few-atom-thick boron nitride layer. The graphene carrier density is tuned across the charge neutrality point (CNP) by a gate, while the SWNT remains degenerate. At high temperatures, the drag resistance changes sign across the CNP, as expected for momentum transfer from drive to drag layer, and exhibits layer exchange Onsager reciprocity.

Guiding Dirac Fermions in Graphene with a Carbon Nanotube.

Relativistic massless charged particles in a two-dimensional conductor can be guided by a one-dimensional electrostatic potential, in an analogous manner to light guided by an optical fiber. We use a carbon nanotube to generate such a guiding potential in graphene and create a single mode electronic waveguide. The nanotube and graphene are separated by a few nanometers and can be controlled and measured independently.

Bone marrow-derived mesenchymal stromal cells harness purinergenic signaling to tolerize human Th1 cells in vivo.

The use of bone marrow-derived mesenchymal stromal cells (BMSC) in the treatment of alloimmune and autoimmune conditions has generated much interest, yet an understanding of the therapeutic mechanism remains elusive. We therefore explored immune modulation by a clinical-grade BMSC product in a model of human-into-mouse xenogeneic graft-versus-host disease (x-GVHD) mediated by human CD4(+) Th1 cells. BMSC reversed established, lethal x-GVHD through marked inhibition of Th1 cell effector function.