22 papers · ranked by Valyu relevance
Eli N. Weinstein, Alan N. Amin, Will Grathwohl, Daniel Kassler + 2 more
Generative probabilistic models of biological sequences have widespread existing and potential applications in analyzing, predicting and designing proteins, RNA and genomes. To test the predictions of such a model experimentally, the standard approach is to draw samples, and then synthesize each sample individually in…
Krzysztof M. Nowak, Robert E. Przekop, Stefano Mariani
Highlights 1. Automated “data factory” is one of the solutions to the data starvation problem in AI-driven discovery of polymers and composites. 2. Robotic platforms with in-line rheology produce thousands of standardized material variants annually at low cost. 3. Continuous Material Management with physical tagging…
Fred Bruford, Frederik Blang, Shahan Nercessian
Systems for synthesizer sound matching, which automatically set the parameters of a synthesizer to emulate an input sound, have the potential to make the process of synthesizer programming faster and easier for novice and experienced musicians alike, whilst also affording new means of interaction with synthesizers.…
Kinyugo Maina
In this paper, we present Msanii, a novel diffusion-based model for synthesizing long-context, highfidelity music efficiently. Our model combines the expressiveness of mel spectrograms, the generative capabilities of diffusion models, and the vocoding capabilities of neural vocoders. We demonstrate the effectiveness of…
Siqin Peng, Xi Chen, Guanhua Wu, Ming Li + 2 more
'Angelos Filippatos'] Because of the high cost of experimental data acquisition, the limited size of the sample set available when conducting tissue structure ultrasound evaluation can cause the evaluation model to have low accuracy. To address such a small-sample problem, the sample set size can be expanded by using…
Eli N. Weinstein, Mattia G. Gollub, Andrei Slabodkin, Cameron L. Gardner + 5 more
We introduce a method to reduce the cost of synthesizing proteins and other biological sequences designed by a generative model by as much as a trillion-fold. In particular, we make our generative models manufacturing-aware, such that model-designed sequences can be efficiently synthesized in the real world with…
Mingyuan Xu, Qirui Deng, Hao Zhang, Anjie Qiao + 4 more
Protein hydrolysis targeting chimeric (PROTAC) has emerged as a promising technology in degrading disease-related proteins for drug design. Recent deep generative models can accelerate PROTAC design, but the generated molecules are often difficult to synthesize. Here we develop SynPROTAC model, which employs Graphormer…
Zhen Ye, Wei Xue, Xu Tan, Qifeng Liu + 1 more
Developing digital sound synthesizers is crucial to the music industry as it provides a low-cost way to produce high-quality sounds with rich timbres. Existing traditional synthesizers often require substantial expertise to determine the overall framework of a synthesizer and the parameters of submodules. Since expert…
Nayeon Kim, Hyuk Jun Yoo, Daeho Kim, Heeseung Lee + 1 more
Autonomous laboratories hold great promise for accelerating material discovery but are often restricted by static, predefined experimental constraints. We present SPACESHIP, an AIdriven framework for dynamic, constraint-free exploration of synthesizable regions in chemical parameter spaces. SPACESHIP integrates…
Zhe Zhang, Taketo Akama
GANStrument, exploiting GANs with a pitch-invariant feature extractor and instance conditioning technique, has shown remarkable capabilities in synthesizing realistic instrument sounds. To further improve the reconstruction ability and pitch accuracy to enhance the editability of user-provided sound, we propose…
Jacob T. Rapp, Bennett J. Bremer, Philip A. Romero
Protein engineering has nearly limitless applications across chemistry, energy and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive and inefficient. Here we present the Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) platform for fully…
Jeff Guo, Philippe Schwaller
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy desired objectives under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased…
Jeff Guo, Philippe Schwaller
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy desired objectives under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased…
Jacob T. Rapp, Bennett J. Bremer, Philip A. Romero
Protein engineering has nearly limitless applications across chemistry, energy, and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive, and inefficient. In this work, we present the Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) platform for…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Ben J. Hayes, Charalampos Saitis, György Fazekas
Many audio synthesizers can produce the same signal given different parameter configurations, meaning the inversion from sound to parameters is an inherently ill-posed problem. We show that this is largely due to intrinsic symmetries of the synthesizer, and focus in particular on permutation invariance. First, we…
Authors not listed
This comprehensive review examines the evolution of autonomous materials synthesis laboratories that integrate artificial intelligence with advanced robotics to accelerate discovery. Traditional materials development pipelines typically require 10-20 years, but self-driving laboratories (SDLs) and Materials…
Chang’en Han, Xinghua Dong, Wang Zhang, Xiaoxia Huang + 3 more
'Chunjian Su' 'Dong-Joo Kim'] Inorganic nanomaterials are pivotal foundational materials driving traditional industries’ transformation and emerging sectors’ evolution. However, their industrial application is hindered by the limitations of conventional synthesis methods, including poor batch stability, scaling…
Authors not listed
Computer-aided synthesis planning aims to identify viable synthetic routes from a target compound to readily available building blocks by iteratively decomposing molecules into smaller precursors. Self-play search algorithms, trained with simulated experience, reach state-of-the-art performance. However, these methods…
Combes, Paolo, Weinzierl, Stefan + 2 more
Deep learning appears as an appealing solution for Automatic Synthesizer Programming (ASP), which aims to assist musicians and sound designers in programming sound synthesizers. However, integrating software synthesizers into training pipelines is challenging due to their potential non-differentiability. This work…
Zhimian Hao, Chonghuan Zhang, Alexei Lapkin
We propose a workflow for reduction in the time required for data generation during generation of statistical digital twins. This methodology is particularly relevant for real-world engineering problems when data generation is expensive. A prerequisite for building surrogates is sufficient input/output data, whereas…
Tarun Khajuria, Kadi Tulver, Jaan Aru
Human vision is not merely a passive process of interpreting sensory input but can also function as a problem-solving process incorporating generative mechanisms to interpret ambiguous or noisy data. This synergy between the generative and discriminative components, often described as analysis-by-synthesis, enables…