Quantum machine learning boosts drug discovery efficiency.
problem Enhancing drug discovery through quantum computing.
method Quantum neural networks on gate-based quantum computers.
result Significant advancements in molecular property prediction and generation.
A new model designs molecular latent vectors for drug discovery.
problem Designing effective molecular descriptors from molecular structures.
method Proposes a denoising diffusion probabilistic model (DDPM) for variational autoencoding molecular graphs.
result Demonstrates superior prediction performance and robustness compared to existing approaches.
Visualizes deep generative models for drug design.
problem Limited visualization tools for deep generative models in drug discovery.
method Proposes a visualization framework for deep graph generative models.
result Interactive visualization and molecular optimization tools.
MoFlow generates chemically valid molecular graphs from latent representations.
problem Generating chemically valid molecular graphs from latent representations is challenging.
method MoFlow uses a flow-based approach with Glow for bond generation and a novel graph conditional flow for atom generation, ensuring chemical validity and efficiency.
result MoFlow achieves state-of-the-art performance in molecular graph generation and optimization.
AI helps in drug discovery with understandable explanations.
problem Understanding the complex models behind AI-generated drugs.
method Explainable AI methods to interpret deep learning models.
result Improved interpretability of AI-generated drug properties.
CardiGraphormer uses SSL and GNNs to improve drug discovery.
problem Challenges in drug discovery due to combinatorial chemical space and limited approved drugs.
method Combines self-supervised learning, Graph Neural Networks, and Cardinality Preserving Attention.
result Enhanced predictive performance and interpretability in drug discovery.
Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care and many others. In chemistry, conventional methodologies for innovation usually rely on expensive and incremental strategies to optimize properties from molecular structures. On the other hand, inverse approa…
RAMBO optimizes multi-regime problems by discovering and modeling distinct energy basins.
problem Multi-regime problems in molecular conformation and drug discovery.
method Dirichlet Process Mixture of Gaussian Processes with adaptive hyperparameters and concentration parameters.
result Consistent improvements over state-of-the-art on multi-regime objectives.
Model predicts stable molecules with AI and physics constraints.
problem Designing stable molecules with limited data.
method Graph Scattering Variational Autoencoder with physical constraints.
result Model generates stable molecules with desired properties.
DeepGG generates graph distributions for drug discovery and molecular design.
problem Learning graph distributions for various applications.
method Improved deep graph generator based on deep state machines with graph and node embeddings.
result The state machine design favors specific graph distributions.
Paper tackles multi-task learning for molecular property prediction with limited data.
problem Limited labeled data for each molecular property task in drug discovery.
method Proposes SGNN-EBM method to utilize relation graph between tasks and improve multi-task learning performance.
result Empirical results show the effectiveness of SGNN-EBM.
In drug-discovery-related tasks such as virtual screening, machine learning is emerging as a promising way to predict molecular properties. Conventionally, molecular fingerprints (numerical representations of molecules) are calculated through rule-based algorithms that map molecules to a sparse discrete space. However,…
The discovery of functional molecules is an expensive and time-consuming process, exemplified by the rising costs of small molecule therapeutic discovery. One class of techniques of growing interest for early-stage drug discovery is de novo molecular generation and optimization, catalyzed by the development of new deep…
The success of enhanced sampling molecular simulations that accelerate along collective variables (CVs) is predicated on the availability of variables coincident with the slow collective motions governing the long-time conformational dynamics of a system. It is challenging to intuit these slow CVs for all but the simpl…
This work improves molecular design by efficiently selecting diverse candidate molecules.
problem Designing molecules that satisfy multiple conflicting objectives.
method A modular 'generate-then-optimize' framework using generative models and a novel acquisition function.
result Significant improvements in sample efficiency across synthetic and application-driven tasks.
VecMol generates 3D molecules as continuous vector fields, overcoming modality and geometry constraints.
problem Challenges in generating 3D molecules, especially in drug discovery and materials science.
method VecMol reimagines molecular representation by modeling 3D molecules as continuous vector fields over Euclidean space, parameterized by a neural field and generated using a latent diffusion model.
result Vector-field-based representations show promise for 3D molecular generation, validated on benchmarks.
Graph Polish optimizes molecular structures by minimizing changes and maximizing preservation.
problem Error-prone traditional molecular optimization methods.
method Graph Polish transforms optimization into a polishing task, focusing on optimization centers and minimizing changes.
result Significant advantage over state-of-the-art methods on multiple optimization tasks.
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-d…
HiGraphDTI learns drug and target representations from molecular graphs to predict DTIs.
problem Inaccurate drug-target interaction prediction due to insufficient chemical information extraction.
method Hierarchical graph representation learning to extract chemical information from atoms, motifs, and molecules.
result HiGraphDTI outperforms state-of-the-art methods in DTI prediction and interaction interpretation.
New method uses Riemannian geometry to describe molecular shapes.
problem Predicting drug-like molecules using shape similarity.
method Riemannian geometry applied to molecular surfaces.
result RGMolSA method captures molecular shape effectively.
This review explores the use of machine learning in discovering collective variables for biomolecular dynamics.
problem Understanding the conformational dynamics and molecular recognition in biomolecules.
method Statistical analysis of high-dimensional spatiotemporal data generated from molecular dynamics simulations.
result Machine learning algorithms can be used to discover abstract collective variables that describe biomolecular dynamics.
A Graph Neural Network model for generating molecular graphs.
problem Designing new drug molecules efficiently and cost-effectively.
method Sequential molecular graph generator based on Graph Neural Networks.
result The model can generate molecular graphs without overfitting and outperforms existing methods.
CSLVAE generates large chemical libraries efficiently.
problem Navigating ultra-large combinatorial synthesis libraries.
method Hierarchically-organized database with molecular encoder and decoder.
result Generates valid molecular graphs without autoregression.
New dataset abla2DFT for drug-like molecules benchmarks neural network potentials.
problem Lack of large, diverse datasets for training neural network potentials in quantum chemistry.
method Developed a new dataset abla2DFT containing energies, forces, and molecular properties for drug-like molecules. result First dataset with relaxation trajectories for drug-like molecules.
Automating molecular design using deep reinforcement learning (RL) holds the promise of accelerating the discovery of new chemical compounds. Existing approaches work with molecular graphs and thus ignore the location of atoms in space, which restricts them to 1) generating single organic molecules and 2) heuristic rew…
BEACON optimizes discovery by efficiently finding novel behaviors.
problem Discovering diverse system behaviors without a scalar objective.
method Bayesian optimization inspired strategy using multi-output Gaussian processes.
result BEACON discovers broader sets of distinct behaviors than competing methods.
Study evaluates uncertainty quantification methods for molecular property prediction.
problem Uncertainty in neural models for molecular property prediction.
method Systematically evaluated several UQ methods on five benchmark datasets.
result No single method is unequivocally superior, and none provides reliable error ranking across datasets.
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 …
DOCKSTRING simplifies docking simulations for better drug design benchmarks.
problem Lack of meaningful benchmarks for ligand design.
method Open-source Python package for docking scores, extensive dataset, and pharmaceutically-relevant tasks.
result Docking scores are more appropriate benchmarks than simple physicochemical properties.
Molecule property prediction is a fundamental problem for computer-aided drug discovery and materials science. Quantum-chemical simulations such as density functional theory (DFT) have been widely used for calculating the molecule properties, however, because of the heavy computational cost, it is difficult to search a…
CGD improves diffusion models' out-of-distribution generalization.
problem Reliable sampling from high-value regions beyond training data.
method Context-guided diffusion (CGD) using unlabeled data and smoothness constraints.
result Substantial performance gains across various diffusion processes.
XIMP improves molecular property prediction by integrating multiple graph representations.
problem Graph neural networks struggle in data-scarce regimes and fail to surpass traditional methods.
method Cross-graph inter-message passing with multiple graph abstractions.
result XIMP outperforms state-of-the-art baselines across diverse molecular property tasks.
Quantum computing promises faster bioinformatics, but challenges remain.
problem Efficient bioinformatics processing and drug discovery.
method Quantum algorithms for optimization, simulation, and machine learning.
result Quantum computing can significantly speed up bioinformatics tasks.
RC flow learns molecular kinetics in low dimensions.
problem Discovering interpretable low-dimensional models of molecular kinetics.
method Normalizing flow for coordinate transformation and Brownian dynamics for kinetics approximation.
result Tractable and trainable model of reduced kinetics in continuous time and space.
New method uses Riemannian geometry to quantify molecular shapes.
problem Quantifying molecular similarity for drug discovery.
method Riemannian geometry and Kähler quantization (KQMolSA).
result KQMolSA method compares well to existing shape similarity methods.
Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery. We cast molecular optimization as a translation problem, where the goal is to map an input compound to a target compound with improved biochemical properties. Remarkably, we observe that when generated mole…
BERT learns molecular substructures for chemistry problems.
problem Predicting chemical properties and synthesizing molecules.
method Transformer-based BERT model on molecule string representations, attention visualization.
result BERT learns to represent functional groups and atoms for various chemical properties.
Molecular structure-property relationships are key to molecular engineering for materials and drug discovery. The rise of deep learning offers a new viable solution to elucidate the structure-property relationships directly from chemical data. Here we show that the performance of graph convolutional networks (GCNs) for…
The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize …
Functional groups (FGs) are molecular substructures that are served as a foundation for analyzing and predicting chemical properties of molecules. Automatic discovery of FGs will impact various fields of research, including medicinal chemistry and material sciences, by reducing the amount of lab experiments required fo…
Data-driven approach discovers molecular photoswitches with separated electronic absorption bands.
problem Engineering photoswitchable molecules with specific electronic absorption bands remains challenging.
method Data-driven discovery pipeline using Gaussian processes for multitask learning.
result Multioutput Gaussian process (MOGP) trained on four photoswitch transition wavelengths outperforms single-task models and TD-DFT.
Study compares GNNs and classical molecular featurisations for molecular property and cliff prediction.
problem Comparing GNNs and classical featurisations for molecular property and cliff prediction.
method Systematic exploration and comparison of PDVs, ECFPs, and GNNs; introduction of substructure pooling.
result Sort & Slice outperforms hash-based folding in ECFP vectorization.
Gradient GA uses gradient information to improve molecular design.
problem Random walk exploration limits genetic algorithms' quality and speed in molecular design.
method Gradient GA incorporates gradient information from the objective function into genetic algorithms, using a differentiable neural network and Discrete Langevin Proposal.
result Significantly improves convergence speed and solution quality over traditional genetic algorithms.
Graph Energy Matching improves generation quality for molecular graphs.
problem Discrete energy-based models struggle with efficient and high-quality sampling for graph generation.
method Inspired by transport-map optimization, Graph Energy Matching learns a permutation-invariant potential energy to guide sampling.
result GEM matches or surpasses discrete diffusion baselines on molecular graph benchmarks.
The prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) of small molecules from their molecular structure is a central problem in medicinal chemistry with great practical importance in drug discovery. Creating predictive models conventionally requires substantial trial-and-error for the …
Enhances diffusion-based sampling for molecular systems.
problem Inefficiency and thermodynamic mode miss in diffusion-based samplers for molecular systems.
method Introduces a sequential bias along collective variables (CVs) to encourage exploration and increase temperature in the projected space.
result Improves efficiency, mode discovery, and free energy estimation; first to demonstrate reactive sampling.
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…
CoDrug uses KDE to create valid prediction sets for drug molecules under covariate shift.
problem Creating reliable uncertainty estimates for drug properties from computational models.
method CoDrug employs an energy-based model and KDE to assess and rectify distribution shift.
result CoDrug reduces the coverage gap by over 35% compared to non-adjusted conformal prediction sets.