A Self-Improving Photosensitizer Discovery System via Bayesian Optimization and Quantum Chemical Calculation

Artificial intelligence (AI) based self-learning or self-improving material discovery system is the holy grail of next-generation material discovery and materials science. Herein, we demonstrate how to combine accurate prediction of material performance via quantum chemical calculations and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PS). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean average error of 0.09 eV for singlet-triplet spitting) and high-performance PS search ability, realizing the efficient discovery of PS. From a molecular space with more than 7 million molecules, 5950 potential high-performance PSs were discovered.

A Self-Improving Photosensitizer Discovery System via Bayesian Optimization and Quantum Chemical Calculation

Artificial intelligence (AI) based self-learning or self-improving material discovery system is the holy grail of next-generation material discovery and materials science. Herein, we demonstrate how to combine accurate prediction of material performance via quantum chemical calculations and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PS). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean average error of 0.09 eV for singlet-triplet spitting) and high-performance PS search ability, realizing the efficient discovery of PS. From a molecular space with more than 7 million molecules, 5950 potential high-performance PSs were discovered.

Potent macrocycle inhibitors of the human SAGA deubiquitinating module

The SAGA complex is a transcriptional coactivator that plays multiple roles in activating transcription and is conserved from yeast to humans. One of SAGAs activities is the removal of ubiquitin from histone H2B-K120 by the deubiquitinating module (DUBm), a four-protein subcomplex containing the catalytic subunit, USP22, bound to three proteins that are required for catalytic activity and targeting to nucleosomes. Overexpression of USP22 is correlated with cancers with a poor prognosis that are resistant to available therapies. We used the RaPID (Random non-standard Peptides Integrated Discovery) system to identify cyclic peptides that are potent and highly specific inhibitors of USP22. Peptide binding did not impact the overall integrity of the DUBm complex as judged by small-angle x-ray scattering, indicating that the inhibitors do not disrupt subunit interactions required for USP22 activity. Cells treated with peptide had increased levels of H2B monoubiquitination, demonstrating the ability of the cyclic peptides to enter human cells and inhibit H2B deubiquitination. The macrocycle inhibitors we have identified in this work thus exhibit favorable drug-like properties and constitute, to our knowledge, the first reported inhibitors of USP22/SAGA DUB module.

A New Approach Customizable Distributed Network Service Discovery System

Computer systems and applications on the internet provide services to outsiders and, at the same time, the vulnerabilities may be exploited by attackers and leak some sensitive private information. To collect and monitor the service information provided by the network environment such as IoT (Internet of Things), vehicular networks, cloud computing, and cloud storage, it is particularly important that a system can provide faster service discovery for discovering and identifying specific network services. The current service discovery systems mainly use port scanning technology, including Nmap, Zmap, and Masscan. However, these technologies hard code the service features and only support common services so that cannot cope with real-time updates and changing network services. To solve the above problems, this paper proposed a customizable distributed network service discovery system based on stateless scanning technology of Masscan and proposed a customizable interactive pattern set syntax. The system used random destination address technologies to scan for Ipv4 address allocation and used a distributed deployment scheme. Experimental results show that the system has high scanning speed and has high adaptability to new services and special services.

A Reactivity First Approach to Autonomous Discovery of New Chemistry

<p></p><p></p><p>We present a robotic chemical discovery system capable of learning the generalized notion of reactivity using a neural network model that can abstract the reactivity from the identity of the reagents. The system is controlled using an algorithm that works in conjunction with this learned knowledge, the robot was able to autonomously explore a large number of potential reactions and assess the reactivity of mixtures, including unknown datasets, regardless the identity of the starting materials. The system identified a range of chemical reactions and products, some of which were well-known, some new but predictable from known pathways, but also some unpredictable reactions that yielded new molecules. The search was done within a budget of 15 inputs combined in 1018 reactions, which allowed us not only to discover a new photochemical reaction, but also a new reactivity mode for a well-known reagent (<i>p</i>-toluenesulfonylmethyl isocyanide, TosMIC). This involved the reaction of six equivalents of TosMIC in a ‘multi-step, single-substrate’ cascade reaction yielding a trimeric product in high yield (47% unoptimized) with formation of five new C-C bonds involving <i>sp</i>-<i>sp²</i> and <i>sp</i>-<i>sp³</i> carbon centres. Analysis reveals that this transformation is intrinsically unpredictable, demonstrating the possibility of reactivity-first robotic discovery of unknown reaction methodologies without requiring human input.</p><br><p></p><p></p>