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.
A new model designs molecules with desired properties.
Paper proposes a method to design molecules with specific properties.
Bayesian optimization improves molecule design by addressing three pitfalls.
Generative neural network designs novel 3D molecules with specified properties.
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…
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.
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…
A neural network and evolutionary algorithm framework designs nonlinear optical molecules.
SILVR generates new molecules fitting protein binding sites.
MoleculeSTM learns from molecule structures and texts for better drug design.
A new deep model generates molecules by fragments, improving validity and uniqueness.
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…
New RL method designs 3D molecules with improved symmetry.
Benchmark proposes to assess molecule docking efficiency.
Graphs are ubiquitous data structures for representing interactions between entities. With an emphasis on the use of graphs to represent chemical molecules, we explore the task of learning to generate graphs that conform to a distribution observed in training data. We propose a variational autoencoder model in which bo…
GCDM generates valid large 3D molecules and optimizes existing molecules.
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…
Model predicts stable molecules with AI and physics constraints.
A new RL framework optimizes drug-like molecules synthetically.
Improved RL model for fragment-based molecule generation.
EHVI outperforms scalarized EI in MOBO for molecule design.
Method scales up ML science by measuring multiple molecules at once.
CRPS improves GP-based sequential design for chemical space.
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…
Drug discovery aims to find novel compounds with specified chemical property profiles. In terms of generative modeling, the goal is to learn to sample molecules in the intersection of multiple property constraints. This task becomes increasingly challenging when there are many property constraints. We propose to offset…
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.…
Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN - a CycleGAN-based model that generates optimized compo…
Paper introduces a graph-based approach for retrosynthesis prediction.
MAT uses attention mechanism for molecule property prediction.
In this paper, we presented a novel convolutional neural network framework for graph modeling, with the introduction of two new modules specially designed for graph-structured data: the -th order convolution operator and the adaptive filtering module. Importantly, our framework of High-order and Adaptive Graph Convo…
Drug discovery projects entail cycles of design, synthesis, and testing that yield a series of chemically related small molecules whose properties, such as binding affinity to a given target protein, are progressively tailored to a particular drug discovery goal. The use of deep learning technologies could augment the …
In applications such as molecule design or drug discovery, it is desirable to have an algorithm which recommends new candidate molecules based on the results of past tests. These molecules first need to be synthesized and then tested for objective properties. We describe ChemBO, a Bayesian optimization framework for ge…
New method designs antimicrobial peptides with high potency and low toxicity.
Transfer learning boosts chemically accurate neural network potentials for organic molecules.
Machine learning techniques have recently been adopted in various applications in medicine, biology, chemistry, and material engineering. An important task is to predict the properties of molecules, which serves as the main subroutine in many downstream applications such as virtual screening and drug design. Despite th…
Novel RL approach for molecular design using quantum mechanics.
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 …
The new wave of successful generative models in machine learning has increased the interest in deep learning driven de novo drug design. However, assessing the performance of such generative models is notoriously difficult. Metrics that are typically used to assess the performance of such generative models are the perc…
Automates GNN design for molecular property prediction.
Deep learning predicts RNA degradation from crowdsourced data.
LaMBO optimizes biological sequences using autoencoders and Bayesian optimization.
Visualizes deep generative models for drug design.
We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings …
Deep generative model discovers inhibitors for unknown targets.
This work improves molecular design by efficiently selecting diverse candidate molecules.
CoDrug uses KDE to create valid prediction sets for drug molecules under covariate shift.
We seek to automate the design of molecules based on specific chemical properties. Our primary contributions are a simpler method for generating SMILES strings guaranteed to be chemically valid, using a combination of a new context-free grammar for SMILES and additional masking logic; and casting the molecular property…
When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously-collected spectra to identify the molecule. While popular, this approach will fail to identify molecules that are not in the existing library. In r…