Quantum-computing-enhanced algorithm unveils potential KRAS inhibitors.

We introduce a quantum-classical generative model for small-molecule design, specifically targeting KRAS inhibitors for cancer therapy. We apply the method to design, select and synthesize 15 proposed molecules that could notably engage with KRAS for cancer therapy, with two holding promise for future development as inhibitors. This work showcases the potential of quantum computing to generate experimentally validated hits that compare favorably against classical models.

Performance Prediction of High-Entropy Perovskites LaSrMnCoFeO with Automated High-Throughput Characterization of Combinatorial Libraries and Machine Learning.

Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition-structure-performance relationships of high-entropy LaSrMnCoFeO perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells.