Generalizable, Fast, and Accurate DeepQSPR with fastprop Part 1: Framework and Benchmarks
- URL: http://arxiv.org/abs/2404.02058v1
- Date: Tue, 2 Apr 2024 15:57:32 GMT
- Title: Generalizable, Fast, and Accurate DeepQSPR with fastprop Part 1: Framework and Benchmarks
- Authors: Jackson Burns, William Green,
- Abstract summary: The paper introduces fastprop, a DeepQSPR framework which uses a cogent set of molecular level descriptors to meet and exceed the performance of learned representations on diverse datasets in dramatically less time.
- Score: 0.3683202928838613
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantitative Structure Property Relationship studies aim to define a mapping between molecular structure and arbitrary quantities of interest. This was historically accomplished via the development of descriptors which requires significant domain expertise and struggles to generalize. Thus the field has morphed into Molecular Property Prediction and been given over to learned representations which are highly generalizable. The paper introduces fastprop, a DeepQSPR framework which uses a cogent set of molecular level descriptors to meet and exceed the performance of learned representations on diverse datasets in dramatically less time. fastprop is freely available on github at github.com/JacksonBurns/fastprop.
Related papers
- Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property Prediction [5.171406201338042]
Our research introduces a Hierarchical Prompted Molecular Representation Learning Framework (HiPM)
HiPM enhances the differential expression of tasks in molecular representations through task-aware prompts.
Experiments demonstrate that HiPM achieves state-of-the-art performance across various multi-label datasets.
arXiv Detail & Related papers (2024-05-29T03:10:21Z) - Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction [7.302312984575165]
This paper proposes a novel meta-learning FSMPP framework (KRGTS)
KRGTS comprises the Knowledge-enhanced Relation Graph module and the Task Sampling module.
Empirically, extensive experiments on five datasets demonstrate the superiority of KRGTS over a variety of state-of-the-art methods.
arXiv Detail & Related papers (2024-05-24T13:31:19Z) - Building Interpretable and Reliable Open Information Retriever for New
Domains Overnight [67.03842581848299]
Information retrieval is a critical component for many down-stream tasks such as open-domain question answering (QA)
We propose an information retrieval pipeline that uses entity/event linking model and query decomposition model to focus more accurately on different information units of the query.
We show that, while being more interpretable and reliable, our proposed pipeline significantly improves passage coverages and denotation accuracies across five IR and QA benchmarks.
arXiv Detail & Related papers (2023-08-09T07:47:17Z) - Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular
Property Prediction [53.06671763877109]
We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction.
Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations.
On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
arXiv Detail & Related papers (2023-02-04T01:32:40Z) - Feature construction using explanations of individual predictions [0.0]
We propose a novel approach for reducing the search space based on aggregation of instance-based explanations of predictive models.
We empirically show that reducing the search to these groups significantly reduces the time of feature construction.
We show significant improvements in classification accuracy for several classifiers and demonstrate the feasibility of the proposed feature construction even for large datasets.
arXiv Detail & Related papers (2023-01-23T18:59:01Z) - StructVPR: Distill Structural Knowledge with Weighting Samples for
Visual Place Recognition [49.58170209388029]
Visual place recognition (VPR) is usually considered as a specific image retrieval problem.
We propose StructVPR, a novel training architecture for VPR, to enhance structural knowledge in RGB global features.
Ours achieves state-of-the-art performance while maintaining a low computational cost.
arXiv Detail & Related papers (2022-12-02T02:52:01Z) - Nested Named Entity Recognition as Holistic Structure Parsing [92.8397338250383]
This work models the full nested NEs in a sentence as a holistic structure, then we propose a holistic structure parsing algorithm to disclose the entire NEs once for all.
Experiments show that our model yields promising results on widely-used benchmarks which approach or even achieve state-of-the-art.
arXiv Detail & Related papers (2022-04-17T12:48:20Z) - Temporal and Object Quantification Networks [95.64650820186706]
We present a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex relational-temporal events.
We demonstrate that TOQ-Nets can generalize from small amounts of data to scenarios containing more objects than were present during training and to temporal warpings of input sequences.
arXiv Detail & Related papers (2021-06-10T16:18:21Z) - Learning Concepts Described by Weight Aggregation Logic [0.0]
We introduce an extension of first-order logic that allows to aggregate weights ofs, compare such aggregates, and use them to build more complex formulas.
We show that concepts definable in FOWA1 over a weighted background structure at most polylogarithmic degree are agnostically PAC-learnable in polylogarithmic time after pseudo-linear time preprocessing.
arXiv Detail & Related papers (2020-09-22T14:32:42Z) - Torch-Struct: Deep Structured Prediction Library [138.5262350501951]
We introduce Torch-Struct, a library for structured prediction.
Torch-Struct includes a broad collection of probabilistic structures accessed through a simple and flexible distribution-based API.
arXiv Detail & Related papers (2020-02-03T16:43:02Z)
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.