Generative autoencoders help create new molecular structures.
problem Generating novel molecules with desirable properties.
method Used autoencoders to map molecule structures into a latent space and back.
result The latent space preserves chemical similarity and can generate new compounds.
Generative model creates drug-like molecules with multiple properties.
problem Designing molecules with multiple desired properties.
method Conditional Variational Autoencoder (VAE) in latent space control.
result Can generate drug-like molecules with five target properties.
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.
GEGL uses genetic experts to improve deep learning for molecular design.
problem Designing molecules with desired properties using deep learning.
method Genetic expert-guided learning (GEGL) framework for training DNN.
result Significantly improves molecular design, achieving high scores on benchmarks.
ReLeaSE uses deep reinforcement learning to design novel molecules.
problem Designing molecules with specific properties.
method ReLeaSE integrates generative and predictive deep neural networks trained separately but jointly to generate novel chemical structures.
result ReLeaSE can generate chemical libraries with desired properties.
Advanced GNNs improve molecular generation models.
problem Generating complete graphs with multiple nodes and edges based on labels.
method Replaced standard GNNs with more expressive GNNs in autoregressive and one-shot generation models.
result Advanced GNNs can improve performance of graph generative models, but expressiveness is not a necessity.
Novel RL approach for molecular design using quantum mechanics.
problem Existing RL methods for molecular design are limited in scope and reward function.
method Formulation in Cartesian coordinates, direct use of quantum mechanics for reward function, translation and rotation invariant state-action space.
result Agent efficiently learns to solve molecular design tasks from scratch.
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…
EHVI outperforms scalarized EI in MOBO for molecule design.
problem Benchmarking MOBO strategies for molecule design.
method Compared EHVI against fixed-weight scalarized EI in MOBO.
result EHVI consistently outperforms scalarized EI in molecular optimization tasks.
MolecularRNN generates realistic molecules with desired properties.
problem Designing new molecules with specific properties.
method Graph recurrent generative model with likelihood pretraining and policy gradient tuning.
result Significant distribution shift to desired ranges for lipophilicity, drug-likeness, and melting point.
Benchmark proposes to assess molecule docking efficiency.
problem Lack of realistic benchmarks for measuring progress in drug design.
method Proposes a docking-based benchmark using SMINA software.
result Graph-based generative models fail to generate high-scoring molecules.
MolHF generates complex molecules with hierarchical flow-based model.
problem Designing novel molecular structures with desired properties.
method MolHF is a hierarchical normalizing flow model that generates molecular graphs in a coarse-to-fine manner.
result MolHF achieves state-of-the-art performance in random generation and property optimization.
Heteroencoders improve chemical latent space diversity and molecular generation.
problem Improving chemical latent space properties and diversity in autoencoders.
method Employing SMILES enumeration for encoder or decoder, training RNNs with LSTM, and using QSAR models.
result Heteroencoders yield more diverse latent spaces and better molecular generation.
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.
Deep AA generates latent archetypes from datasets.
problem Representing high-dimensional datasets in understandable basic entities.
method Extends linear Archetypal Analysis with deep learning capabilities.
result Reduces dependence on expert knowledge and handles side information.
New method designs antimicrobial peptides with high potency and low toxicity.
problem Designing potent antimicrobial drugs with low toxicity.
method CLaSS method using deep generative autoencoder and atomistic simulations.
result Design and synthesis of two novel AMPs with high potency and low toxicity.
DeepNovoV2 improves de novo peptide sequencing from mass spectrometry data.
problem De novo peptide sequencing from mass spectrometry data for personalized cancer vaccines.
method DeepNovoV2 combines T-Net and recurrent neural networks for end-to-end training and prediction.
result DeepNovoV2 achieves 13.01-23.95\% higher accuracy than previous methods.
CogMol designs novel drug-like molecules for SARS-CoV-2 targets.
problem Designing efficient drugs for novel viral proteins.
method End-to-end framework combining VAE, controlled sampling, and predictors.
result Highly selective and affinity molecules for SARS-CoV-2 targets.
New models suggest molecules that are often unfeasible to synthesize.
problem Models suggest molecules that are difficult to synthesize.
method Used a computer-aided synthesis planning program to analyze synthesizability of molecules generated by state-of-the-art models.
result State-of-the-art models generate molecules that are often unfeasible to synthesize.
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.
Few-step protein backbone generators reduce sampling time by over 20x.
problem Computational bottleneck in diffusion-based protein generation models.
method Score distillation adapted for protein backbone generation, combined with inference time noise modulation.
result Significant reduction in sampling time (20+ fold) while maintaining comparable performance.
Framework designs antiviral drugs using deep learning and RL.
problem Designing effective antiviral drugs for SARS-CoV-2.
method Deep learning framework with conditional molecular generator and RL.
result Framework generates more antiviral ligands than a VAE baseline.
Non-autoregressive method speeds up protein folding prediction 23 times.
problem Generating protein sequences with higher order interactions.
method Discrete diffusion conditioned on 3D structure using ProteinMPNN.
result 23 times speed up in inference without performance loss.
ALMGIG uses adversarial learning to generate and infer novel molecules efficiently.
problem Efficiently generating and inferring novel molecules using graph representations.
method Adversarial learning framework that avoids explicit graph isomorphism, using cycle-consistency loss and multi-graph Graph Isomorphism Network.
result ALMGIG more accurately learns the distribution over the space of molecules and efficiently searches the molecular space.
A new metric assesses generative models for molecules in drug design.
problem Difficulty in evaluating generative models for molecules.
method Fréchet ChemNet Distance (FCD) using a deep neural network trained to predict drug activities.
result FCD detects diversity and similarity of generated molecules to real ones.
GEN generates millions of valid SMILES with high novelty and property conservation.
problem Generating high-quality, de novo molecules in a known chemical space.
method GEN uses bidirectional RNNs with concatenated sub-models to learn and generate SMILES, with online examination to ensure quality.
result GEN can generate SMILES with 95-98% validity, 85-90% novelty, and 95-99% property conservation.
CF-GPS learns policies from logged data by considering counterfactual outcomes.
problem Learning policies from limited real experience in complex environments.
method Assumes logged real experience and models counterfactual outcomes. Uses structural causal models for evaluation.
result Improves policy evaluation and search results on a grid-world task.
fiBAG integrates multiplatform genomic data to identify disease markers.
problem Understanding complex mechanisms underlying human diseases from multiplatform genomic data.
method fiBAG uses Gaussian process models and Bayes factors to identify functional evidence and guide variable selection.
result fiBAG improves detection of disease-related markers compared to non-integrative methods.
New RL method designs 3D molecules with improved symmetry.
problem Lack of 3D information in molecular design.
method Symmetry-aware actor-critic architecture using spherical harmonics.
result Improves generalization and molecule quality.
Researchers use active subspaces to quantify uncertainty in deep generative models for molecular design.
problem Uncertainty quantification in deep generative models for molecular design due to high parameter space.
method Leveraging active subspaces to approximate posterior distribution over low-dimensional parameters.
result The proposed UQ scheme effectively estimates epistemic uncertainty in high-dimensional parameter space without altering model architecture.
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.
This paper reviews deep learning and knowledge-based methods for molecular design.
problem Optimizing molecular properties for scientific advances and process performance.
method Survey of deep learning and knowledge-based methods for molecular design.
result Deep learning models show promise in overcoming computational challenges.
Automates molecule design with a novel variational autoencoder.
problem Designing molecules based on specific chemical properties.
method Junction tree variational autoencoder generating tree-structured scaffolds and combining them into molecules.
result Significantly outperforms previous models on molecular generation and optimization tasks.
DECAF optimizes molecular graphs for ensemble properties, improving drug design accuracy.
problem Designing molecules with ensemble properties rather than single conformations.
method DECAF uses Boltzmann-expected design with decoupled annealing flows to optimize molecular graphs.
result DECAF optimizes molecular graphs to shift ensemble properties towards targets, improving accuracy over single-conformer methods.
Generative models accelerate chemical design from properties to structures.
problem Expensive and incremental strategies for optimizing chemical properties.
method Review of current deep generative models and their application to molecular systems.
result Generative models can expedite the design of novel useful compounds.
Paper proposes a self-training method to generate molecular targets.
problem Challenges in training generative models for complex molecular design.
method Iterative target augmentation using a property predictor and EM iterations.
result Significant gains in molecular design, outperforming previous methods.
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.
New method combines observational and interventional data for causal model learning.
problem Identifying causal structures from observational data alone is limited.
method Continuous optimization and neural networks for integrating observational and interventional data.
result Strong benchmark results on structure recovery tasks.
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.
Automates GNN design for molecular property prediction.
problem Designing and tuning GNN architectures for molecular property prediction is labor-intensive.
method Developed a NAS approach to automatically discover high-performing GNN architectures for MPNNs.
result Automatically discovered MPNNs outperform manually designed GNNs in molecular property prediction.
Enhances molecular design models by fine-tuning uncertainty-guided VAEs.
problem Fine-tuning pre-trained generative models for specific molecular property optimization.
method Uncertainty-guided fine-tuning of variational autoencoders in an active learning setting.
result Uncertainty-guided fine-tuning improves model performance across multiple molecular properties.
Machine learning models simulate molecular spectra and reactions in solvents.
problem Accurate simulation of molecular spectra and reactions in solvent environments.
method Introduced FieldSchNet, a deep neural network for modeling molecular interactions with external fields.
result Demonstrated significant lowering of Claisen rearrangement reaction activation barrier using FieldSchNet.
New molecular design model outperforms existing methods.
problem Designing valid, unique, and novel molecules.
method Adversarially Regularized Autoencoder (ARAE) combining latent variables from VAE and adversarial training from GAN.
result ARAE outperforms conventional models in validity, uniqueness, and novelty.
A new RL framework optimizes drug-like molecules synthetically.
problem Optimizing drug-like molecules for specific criteria.
method Deep Reinforcement Learning framework for chemical space optimization.
result Outperforms existing methods in pharmacological optimization.
Recently exciting progress has been made on protein contact prediction, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolution…
LaPool improves molecular graph representation learning by capturing interaction importance.
problem Lack of efficient intermediate pooling steps in GNNs leads to poor molecular substructure representation.
method LaPool is a novel, data-driven, and interpretable hierarchical graph pooling method that considers node features and graph structure.
result LaPool outperforms recent GNNs on molecular graph prediction and understanding tasks.
New loss function optimization improves training speed and accuracy.
problem Optimizing neural network performance through loss functions.
method Genetic Loss-function Optimization (GLO) using genetic programming and CMA-ES.
result GLO loss functions lead to better performance with fewer training steps.
Generative neural network designs novel 3D molecules with specified properties.
problem Designing molecules with desired properties in chemistry.
method Conditional generative neural network for 3D molecular structures.
result Demonstrated utility in generating novel molecules with specified motifs or composition.