MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning

• URL: http://arxiv.org/abs/2410.11226v1
• Date: Tue, 15 Oct 2024 03:15:05 GMT
• Title: MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
• Authors: Peter Eckmann, Dongxia Wu, Germano Heinzelmann, Michael K Gilson, Rose Yu,
• Abstract summary: Multi-Fidelity Latent space Active Learning (MF-LAL) is a generative modeling framework that integrates a set of oracles with varying cost-accuracy tradeoffs. We show that MF-LAL produces compounds with significantly better binding free energy scores than other single and multi-fidelity approaches.
• Score: 16.48834281521776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current generative models for drug discovery primarily use molecular docking as an oracle to guide the generation of active compounds. However, such models are often not useful in practice because even compounds with high docking scores do not consistently show experimental activity. More accurate methods for activity prediction exist, such as molecular dynamics based binding free energy calculations, but they are too computationally expensive to use in a generative model. To address this challenge, we propose Multi-Fidelity Latent space Active Learning (MF-LAL), a generative modeling framework that integrates a set of oracles with varying cost-accuracy tradeoffs. Unlike previous approaches that separately learn the surrogate model and generative model, MF-LAL combines the generative and multi-fidelity surrogate models into a single framework, allowing for more accurate activity prediction and higher quality samples. We train MF-LAL with a novel active learning algorithm to further reduce computational cost. Our experiments on two disease-relevant proteins show that MF-LAL produces compounds with significantly better binding free energy scores than other single and multi-fidelity approaches.

Related papers

• TFG-Flow: Training-free Guidance in Multimodal Generative Flow [73.93071065307782]
  We introduce TFG-Flow, a training-free guidance method for multimodal generative flow. TFG-Flow addresses the curse-of-dimensionality while maintaining the property of unbiased sampling in guiding discrete variables. We show that TFG-Flow has great potential in drug design by generating molecules with desired properties.
  arXiv  Detail & Related papers  (2025-01-24T03:44:16Z)
• Generative Modeling of Molecular Dynamics Trajectories [12.255021091552441]
  We introduce generative modeling of molecular trajectories as a paradigm for learning flexible multi-task surrogate models of MD from data. We show such generative models can be adapted to diverse tasks such as forward simulation, transition path sampling, and trajectory upsampling.
  arXiv  Detail & Related papers  (2024-09-26T13:02:28Z)
• Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Properties [0.0]
  Multifidelity machine learning (MFML) for quantum chemical (QC) properties has seen strong development in the recent years. This work assesses the use of non-nested training data for two of these multifidelity methods, namely MFML and optimized MFML.
  arXiv  Detail & Related papers  (2024-07-24T08:34:08Z)
• Generating Multi-Modal and Multi-Attribute Single-Cell Counts with CFGen [76.02070962797794]
  We present Cell Flow for Generation, a flow-based conditional generative model for multi-modal single-cell counts. Our results suggest improved recovery of crucial biological data characteristics while accounting for novel generative tasks.
  arXiv  Detail & Related papers  (2024-07-16T14:05:03Z)
• MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling [17.689451879343014]
  Current generative models for drug discovery primarily use molecular docking to evaluate the quality of generated compounds. We propose a multi-fidelity approach, Multi-Fidelity Bind (MFBind), to achieve the optimal trade-off between accuracy and computational cost.
  arXiv  Detail & Related papers  (2024-02-16T00:48:20Z)
• MolTC: Towards Molecular Relational Modeling In Language Models [28.960416816491392]
  We propose a novel framework for Molecular inTeraction prediction following Chain-of-Thought (CoT) theory termed MolTC. Our experiments, conducted across various datasets involving over 4,000,000 molecular pairs, exhibit the superiority of our method over current GNN and LLM-based baselines.
  arXiv  Detail & Related papers  (2024-02-06T07:51:56Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [73.35846234413611]
  In drug discovery, molecular dynamics (MD) simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning (ML) surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding dynamics. We demonstrate the efficiency and effectiveness of NeuralMD, achieving over 1K$times$ speedup compared to standard numerical MD simulations.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• LLamol: A Dynamic Multi-Conditional Generative Transformer for De Novo Molecular Design [0.0]
  "LLamol" is a single novel generative transformer model based on the LLama 2 architecture. We demonstrate that the model adeptly handles single- and multi-conditional organic molecule generation with up to four conditions. In detail, we showcase the model's capability to utilize token sequences for conditioning, either individually or in combination with numerical properties.
  arXiv  Detail & Related papers  (2023-11-24T10:59:12Z)
• Diffusion-Generative Multi-Fidelity Learning for Physical Simulation [24.723536390322582]
  We develop a diffusion-generative multi-fidelity learning method based on differential equations (SDE), where the generation is a continuous denoising process. By conditioning on additional inputs (temporal or spacial variables), our model can efficiently learn and predict multi-dimensional solution arrays.
  arXiv  Detail & Related papers  (2023-11-09T18:59:05Z)
• Mixed Models with Multiple Instance Learning [51.440557223100164]
  We introduce MixMIL, a framework integrating Generalized Linear Mixed Models (GLMM) and Multiple Instance Learning (MIL) Our empirical results reveal that MixMIL outperforms existing MIL models in single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-04T16:42:42Z)
• Value function estimation using conditional diffusion models for control [62.27184818047923]
  We propose a simple algorithm called Diffused Value Function (DVF) It learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers.
  arXiv  Detail & Related papers  (2023-06-09T18:40:55Z)
• LIMO: Latent Inceptionism for Targeted Molecule Generation [14.391216237573369]
  We present Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. Comprehensive experiments show that LIMO performs competitively on benchmark tasks. One of our generated drug-like compounds has a predicted $K_D$ of $6 cdot 10-14$ M against the human estrogen receptor.
  arXiv  Detail & Related papers  (2022-06-17T21:05:58Z)
• Learning continuous models for continuous physics [94.42705784823997]
  We develop a test based on numerical analysis theory to validate machine learning models for science and engineering applications. Our results illustrate how principled numerical analysis methods can be coupled with existing ML training/testing methodologies to validate models for science and engineering applications.
  arXiv  Detail & Related papers  (2022-02-17T07:56:46Z)
• Particle Dynamics for Learning EBMs [83.59335980576637]
  Energy-based modeling is a promising approach to unsupervised learning, which yields many downstream applications from a single model. The main difficulty in learning energy-based models with the "contrastive approaches" is the generation of samples from the current energy function at each iteration. This paper proposes an alternative approach to getting these samples and avoiding crude MCMC sampling from the current model.
  arXiv  Detail & Related papers  (2021-11-26T23:41:07Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Contrastive Model Inversion for Data-Free Knowledge Distillation [60.08025054715192]
  We propose Contrastive Model Inversion, where the data diversity is explicitly modeled as an optimizable objective. Our main observation is that, under the constraint of the same amount of data, higher data diversity usually indicates stronger instance discrimination. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that CMI achieves significantly superior performance when the generated data are used for knowledge distillation.
  arXiv  Detail & Related papers  (2021-05-18T15:13:00Z)
• MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization [51.00815310242277]
  generative models and reinforcement learning approaches made initial success, but still face difficulties in simultaneously optimizing multiple drug properties. We propose the MultI-constraint MOlecule SAmpling (MIMOSA) approach, a sampling framework to use input molecule as an initial guess and sample molecules from the target distribution.
  arXiv  Detail & Related papers  (2020-10-05T20:18:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.