Results 91 to 100 of about 56,397 (288)

Buddha's not smiling

, 2011
Anand, D.
core   +4 more sources

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Generative and Experimental Validation of High Refractive Index Polymers via Domain Knowledge Approach with Small Data

Advanced Intelligent Discovery, EarlyView.
This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo, Seigo Watanabe, Hana Matsunaga, Kenichi Oyaizu   +3 more
wiley   +1 more source

MolMiner: Toward Controllable, Three‐Dimensional‐Aware, Fragment‐Based Molecular Design

Advanced Intelligent Discovery, EarlyView.
MolMiner is a fragment‐based, geometry‐aware, and order‐agnostic generative model for molecular design with strong inductive biases. Using symmetry‐aware fragment assembly, dynamic three‐dimensional geometry, and multi‐property conditioning, MolMiner enables interpretable and controllable molecular generation.
Raul Ortega‐Ochoa, Tejs Vegge, Jes Frellsen   +2 more
wiley   +1 more source

Practical Computational Exploration of Chemical Reaction Space: Assessing the Chemical Stability of Active Pharmaceutical Ingredients Under Acidic Conditions

Angewandte Chemie, EarlyView.
Metadynamics simulations drive automated reaction exploration of acid‐catalysed drug degradation, generating a network spanning 1720 species and 2471 elementary steps. QM‐based filtering reduces the network to eight candidate pathways for rigorous DFT refinement and microkinetic modelling.
Julius Seumer, Maria Harris Rasmussen, Oriana Brea, Mikko Muuronen, Andreas H. Göller, Jan H. Jensen   +5 more
wiley   +2 more sources

Founder smiles increase investor trust and funding

Scientific Reports
Entrepreneurs seeking funding increasingly present themselves to investors for the first time online. In these digital first impressions, some entrepreneurs smile while others do not.
Dimosthenis Stefanidis, Nicos Nicolaou, Scott Shane, Mark Conley, George Pallis, Marios D. Dikaiakos   +5 more
doaj   +1 more source

Artificial Intelligence‐Driven Network Pharmacology: A Methodological Paradigm Shift Bridging Traditional Wisdom and Modern Science

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence is redefining network pharmacology (NP). By integrating knowledge graph engineering, geometric deep learning, multiomics anchoring, and generative reasoning, AI‐driven NP (AI‐NP) transforms static target mapping into dynamic, predictive modeling.
Cong Wang, Sile Hu, Rigahai Ji, Riguna A, Siqige Hao, Ma Zhuo, Yi‐Hung Chen, Bisihalatu Hu, Ulana Bai, Xigui Hada   +9 more
wiley   +1 more source

[Smiling Woman]

Photograph of a smiling ...

core   +2 more sources

Modular Access to α‐Tertiary Amines: Electrochemical Tandem Di‐Functionalization of Olefins With Ammonia Surrogates and Amino Acids

Angewandte Chemie, EarlyView.
A metal‐ and oxidant‐free electrochemical multicomponent di‐functionalization strategy that rapidly converts 1,1‐disubstituted alkenes into α‐tertiary primary amines with simultaneous installation of bio‐relevant sulfur or selenium motifs was devised for late‐stage functionalization. ABSTRACT Quaternary carbon centers, especially those bearing nitrogen,
Adrija Ghosh, Tarun, Sven Erik Peters, Lutz Ackermann, Debasis Banerjee   +4 more
wiley   +2 more sources

Catalyst‐Specialized Chemical Language Model Based on Transformer Variational Autoencoder for Catalyst Design and Discovery

Advanced Intelligent Discovery, EarlyView.
We present CatTransVAE, a catalyst‐specialized chemical language model (CLM) built on a transformer variational autoencoder (VAE), developed through pretraining on general compounds followed by fine‐tuning on diverse catalyst databases. A template‐guided generation framework is introduced to enable controlled catalyst design under structural ...
Apakorn Kengkanna, Masahito Ohue
wiley   +1 more source