Uncovering Neural Scaling Laws in Molecular Representation Learning Article Swipe
Dingshuo Chen
,
Yanqiao Zhu
,
Jieyu Zhang
,
Yuanqi Du
,
Zhixun Li
,
Qiang Liu
,
Shu Wu
,
Liang Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.15123
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.15123
Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field. In this paper, we delve into the neural scaling behaviors of MRL from a data-centric viewpoint, examining four key dimensions: (1) data modalities, (2) dataset splitting, (3) the role of pre-training, and (4) model capacity. Our empirical studies confirm a consistent power-law relationship between data volume and MRL performance across these dimensions. Additionally, through detailed analysis, we identify potential avenues for improving learning efficiency. To challenge these scaling laws, we adapt seven popular data pruning strategies to molecular data and benchmark their performance. Our findings underline the importance of data-centric MRL and highlight possible directions for future research.
Related Topics
Concepts
Variety (cybernetics)
Computer science
Benchmark (surveying)
Data science
Representation (politics)
Pruning
Artificial intelligence
Machine learning
Field (mathematics)
Modalities
Scaling
Law
Mathematics
Political science
Sociology
Agronomy
Politics
Biology
Pure mathematics
Social science
Geodesy
Geometry
Geography
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.15123
- https://arxiv.org/pdf/2309.15123
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387209259
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4387209259Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2309.15123Digital Object Identifier
- Title
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Uncovering Neural Scaling Laws in Molecular Representation LearningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-09-15Full publication date if available
- Authors
-
Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu, Liang WangList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.15123Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2309.15123Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2309.15123Direct OA link when available
- Concepts
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Variety (cybernetics), Computer science, Benchmark (surveying), Data science, Representation (politics), Pruning, Artificial intelligence, Machine learning, Field (mathematics), Modalities, Scaling, Law, Mathematics, Political science, Sociology, Agronomy, Politics, Biology, Pure mathematics, Social science, Geodesy, Geometry, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.MRL | 69, 105, 148 |
| abstract_inverted_index.Our | 93, 141 |
| abstract_inverted_index.and | 12, 24, 45, 89, 104, 137, 149 |
| abstract_inverted_index.for | 10, 118, 153 |
| abstract_inverted_index.has | 4, 29 |
| abstract_inverted_index.key | 76 |
| abstract_inverted_index.not | 51 |
| abstract_inverted_index.the | 39, 64, 85, 144 |
| abstract_inverted_index.yet | 52 |
| abstract_inverted_index.been | 30 |
| abstract_inverted_index.both | 42 |
| abstract_inverted_index.data | 43, 79, 102, 131, 136 |
| abstract_inverted_index.drug | 11 |
| abstract_inverted_index.four | 75 |
| abstract_inverted_index.from | 70 |
| abstract_inverted_index.into | 63 |
| abstract_inverted_index.role | 86 |
| abstract_inverted_index.such | 20 |
| abstract_inverted_index.this | 56, 59 |
| abstract_inverted_index.tool | 9 |
| abstract_inverted_index.(MRL) | 3 |
| abstract_inverted_index.While | 27 |
| abstract_inverted_index.adapt | 128 |
| abstract_inverted_index.delve | 62 |
| abstract_inverted_index.laws, | 126 |
| abstract_inverted_index.model | 91 |
| abstract_inverted_index.seven | 129 |
| abstract_inverted_index.surge | 32 |
| abstract_inverted_index.tasks | 19 |
| abstract_inverted_index.their | 139 |
| abstract_inverted_index.there | 28 |
| abstract_inverted_index.these | 108, 124 |
| abstract_inverted_index.across | 107 |
| abstract_inverted_index.field. | 57 |
| abstract_inverted_index.future | 154 |
| abstract_inverted_index.neural | 65 |
| abstract_inverted_index.paper, | 60 |
| abstract_inverted_index.volume | 103 |
| abstract_inverted_index.within | 55 |
| abstract_inverted_index.avenues | 117 |
| abstract_inverted_index.between | 101 |
| abstract_inverted_index.clearly | 53 |
| abstract_inverted_index.confirm | 96 |
| abstract_inverted_index.dataset | 82 |
| abstract_inverted_index.design. | 26 |
| abstract_inverted_index.emerged | 5 |
| abstract_inverted_index.inverse | 25 |
| abstract_inverted_index.popular | 130 |
| abstract_inverted_index.pruning | 132 |
| abstract_inverted_index.quality | 46 |
| abstract_inverted_index.scaling | 66, 125 |
| abstract_inverted_index.studies | 95 |
| abstract_inverted_index.through | 111 |
| abstract_inverted_index.variety | 17 |
| abstract_inverted_index.virtual | 22 |
| abstract_inverted_index.Learning | 2 |
| abstract_inverted_index.detailed | 112 |
| abstract_inverted_index.findings | 142 |
| abstract_inverted_index.identify | 115 |
| abstract_inverted_index.interest | 34 |
| abstract_inverted_index.learning | 120 |
| abstract_inverted_index.possible | 151 |
| abstract_inverted_index.powerful | 8 |
| abstract_inverted_index.quantity | 44 |
| abstract_inverted_index.Molecular | 0 |
| abstract_inverted_index.advancing | 36 |
| abstract_inverted_index.analysis, | 113 |
| abstract_inverted_index.behaviors | 67 |
| abstract_inverted_index.benchmark | 138 |
| abstract_inverted_index.capacity. | 92 |
| abstract_inverted_index.challenge | 123 |
| abstract_inverted_index.discovery | 14 |
| abstract_inverted_index.empirical | 94 |
| abstract_inverted_index.examining | 74 |
| abstract_inverted_index.highlight | 150 |
| abstract_inverted_index.improving | 119 |
| abstract_inverted_index.influence | 40 |
| abstract_inverted_index.materials | 13 |
| abstract_inverted_index.molecular | 48, 135 |
| abstract_inverted_index.potential | 116 |
| abstract_inverted_index.power-law | 99 |
| abstract_inverted_index.research. | 155 |
| abstract_inverted_index.screening | 23 |
| abstract_inverted_index.underline | 143 |
| abstract_inverted_index.consistent | 98 |
| abstract_inverted_index.directions | 152 |
| abstract_inverted_index.importance | 145 |
| abstract_inverted_index.splitting, | 83 |
| abstract_inverted_index.strategies | 133 |
| abstract_inverted_index.understood | 54 |
| abstract_inverted_index.viewpoint, | 73 |
| abstract_inverted_index.dimensions. | 109 |
| abstract_inverted_index.dimensions: | 77 |
| abstract_inverted_index.efficiency. | 121 |
| abstract_inverted_index.modalities, | 80 |
| abstract_inverted_index.performance | 106 |
| abstract_inverted_index.techniques, | 38 |
| abstract_inverted_index.data-centric | 72, 147 |
| abstract_inverted_index.performance. | 140 |
| abstract_inverted_index.relationship | 100 |
| abstract_inverted_index.Additionally, | 110 |
| abstract_inverted_index.model-centric | 37 |
| abstract_inverted_index.pre-training, | 88 |
| abstract_inverted_index.Representation | 1 |
| abstract_inverted_index.representations | 49 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |