A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
GCDM generates valid large 3D molecules and optimizes existing molecules.
Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method that facilitates generating new molecules with desired properties. The p…
Molecule optimization is about generating molecule with more desirable properties based on an input molecule . The state-of-the-art approaches partition the molecules into a large set of substructures and grow the new molecule structure by iteratively predicting which substructure from to add. However, s…
In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language process…
ConfFlow uses transformer networks to generate molecular conformations efficiently.
A new deep model generates molecules by fragments, improving validity and uniqueness.
Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large (10^23-10^60), a common technique during drug discovery is to start from a molecule which already has some of the desired properties. An interdisciplinary team of scientists generates hypothesis about the required…
Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the tradeoff between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of organic molecules. Our resulting models learn the difference between low…
Paper introduces a graph-based approach for retrosynthesis prediction.
MAT uses attention mechanism for molecule property prediction.
The discovery of novel materials and functional molecules can help to solve some of society's most urgent challenges, ranging from efficient energy harvesting and storage to uncovering novel pharmaceutical drug candidates. Traditionally matter engineering -- generally denoted as inverse design -- was based massively on…
AI and HPC help screen millions of molecules for SARS-CoV-2 treatments.
MoleculeSTM learns from molecule structures and texts for better drug design.
Bio-oil molecule assessment is essential for the sustainable development of chemicals and transportation fuels. These oxygenated molecules have adequate carbon, hydrogen, and oxygen atoms that can be used for developing new value-added molecules (chemicals or transportation fuels). One motivation for our study stems fr…
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.
ERP improves drug discovery by balancing molecule generation quality and efficiency.
Benchmark proposes to assess molecule docking efficiency.
TorsionNet uses reinforcement learning to efficiently generate conformers of flexible molecules.
Paper proposes a method to design molecules with specific properties.
Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models allow one to generate molecules with desirable properties, they give no guarantees that the molecules can actually be synthesized in practice. We propose a new molecu…
MACE architecture outperforms alternatives in various molecular and materials science tasks.
Deep neural network identifies potential SARS-CoV-2 inhibitors.
A new model designs molecules with desired properties.
Machine learning methods have shown promise in predicting molecular properties, and given sufficient training data machine learning approaches can enable rapid high-throughput virtual screening of large libraries of compounds. Graph-based neural network architectures have emerged in recent years as the most successful …
Paper introduces a method to predict molecule properties from diverse data sources.
SILVR generates new molecules fitting protein binding sites.
Transfer learning boosts chemically accurate neural network potentials for organic molecules.
Modof-pipe optimizes molecules by modifying a single site, outperforming state-of-the-art methods.
Improved RL model for fragment-based molecule generation.
We present a sparse knowledge gradient (SpKG) algorithm for adaptively selecting the targeted regions within a large RNA molecule to identify which regions are most amenable to interactions with other molecules. Experimentally, such regions can be inferred from fluorescence measurements obtained by binding a complement…
A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functi…
Generative model learns to create molecules with multiple properties using interpretable substructures.
We present a framework, which we call Molecule Deep -Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double -learning and randomized value functions). We directly define modifications on molecules, thereby ensuring 100…
Chemical space is so large that brute force searches for new interesting molecules are infeasible. High-throughput virtual screening via computer cluster simulations can speed up the discovery process by collecting very large amounts of data in parallel, e.g., up to hundreds or thousands of parallel measurements. Bayes…
Designing new molecules with a set of predefined properties is a core problem in modern drug discovery and development. There is a growing need for de-novo design methods that would address this problem. We present MolecularRNN, the graph recurrent generative model for molecular structures. Our model generates diverse …
In this study, we intend to solve a mutual information problem in interacting molecules of any type, such as proteins, nucleic acids, and small molecules. Using machine learning techniques, we accurately predict pairwise interactions, which can be of medical and biological importance. Graphs are are useful in this prob…
Generative neural network designs novel 3D molecules with specified properties.
Equivariant diffusion model generates 3D molecules efficiently.
Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest. The way as such has called for a strategy of designing molecules retaining a particular scaffold as a substructure. On this account, our present work proposes a scaff…
Recent methods for generating novel molecules use graph representations of molecules and employ various forms of graph convolutional neural networks for inference. However, training requires solving an expensive graph isomorphism problem, which previous approaches do not address or solve only approximately. In this wor…
Bayesian optimization improves molecule design by addressing three pitfalls.
New model generates larger molecules more effectively.
Researchers parallelize neural kernels for large-scale data, achieving state-of-the-art accuracy.
Molecule generation is to design new molecules with specific chemical properties and further to optimize the desired chemical properties. Following previous work, we encode molecules into continuous vectors in the latent space and then decode the vectors into molecules under the variational autoencoder (VAE) framework.…
Deep QMC method accurately computes electronic excited states.
G2Gs transforms target molecules into reactants without templates, improving accuracy.
A new graph model HMG and neural network HMGNN improve molecule property predictions.
Molecular optimization aims to discover novel molecules with desirable properties. Two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllable way due to hard chemical constraints such as the valency conditions, and (ii) it is often costly to evaluate a property of a novel molecu…