MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails

• URL: http://arxiv.org/abs/2208.10083v1
• Date: Mon, 22 Aug 2022 06:40:13 GMT
• Title: MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails
• Authors: Kexin Chen, Guangyong Chen, Junyou Li, Yuansheng Huang, Pheng-Ann Heng
• Abstract summary: In this paper, we focus on the reaction yield prediction problem. We first put forth MetaRF, an attention-based differentiable random forest model specially designed for the few-shot yield prediction. To improve the few-shot learning performance, we further introduce a dimension-reduction based sampling method.
• Score: 58.47364143304643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with high-quality annotations, which seriously limits the wide usage of data-driven methods. In this paper, we focus on the reaction yield prediction problem, which assists chemists in selecting high-yield reactions in a new chemical space only with a few experimental trials. To attack this challenge, we first put forth MetaRF, an attention-based differentiable random forest model specially designed for the few-shot yield prediction, where the attention weight of a random forest is automatically optimized by the meta-learning framework and can be quickly adapted to predict the performance of new reagents while given a few additional samples. To improve the few-shot learning performance, we further introduce a dimension-reduction based sampling method to determine valuable samples to be experimentally tested and then learned. Our methodology is evaluated on three different datasets and acquires satisfactory performance on few-shot prediction. In high-throughput experimentation (HTE) datasets, the average yield of our methodology's top 10 high-yield reactions is relatively close to the results of ideal yield selection.

Related papers

• Optimizing Probabilistic Conformal Prediction with Vectorized Non-Conformity Scores [6.059745771017814]
  We propose a novel framework that enhances efficiency by first vectorizing the non-conformity scores with ranked samples and then optimizing the shape of the prediction set by varying the quantiles for samples at the same rank. Our method delivers valid coverage while producing discontinuous and more efficient prediction sets, making it particularly suited for high-stakes applications.
  arXiv  Detail & Related papers  (2024-10-17T16:37:03Z)
• From Prediction to Action: Critical Role of Performance Estimation for Machine-Learning-Driven Materials Discovery [2.3243389656894595]
  We argue that the lack of proper performance estimation methods from pre-computed data collections is a fundamental problem for improving data-driven materials discovery. We propose a novel such estimator that, in contrast to na"ive reward estimation, successfully predicts Gaussian processes with the "expected improvement" acquisition function.
  arXiv  Detail & Related papers  (2023-11-27T05:29:43Z)
• Data Pruning via Moving-one-Sample-out [61.45441981346064]
  We propose a novel data-pruning approach called moving-one-sample-out (MoSo) MoSo aims to identify and remove the least informative samples from the training set. Experimental results demonstrate that MoSo effectively mitigates severe performance degradation at high pruning ratios.
  arXiv  Detail & Related papers  (2023-10-23T08:00:03Z)
• ReSmooth: Detecting and Utilizing OOD Samples when Training with Data Augmentation [57.38418881020046]
  Recent DA techniques always meet the need for diversity in augmented training samples. An augmentation strategy that has a high diversity usually introduces out-of-distribution (OOD) augmented samples. We propose ReSmooth, a framework that firstly detects OOD samples in augmented samples and then leverages them.
  arXiv  Detail & Related papers  (2022-05-25T09:29:27Z)
• Multimodal Transformer-based Model for Buchwald-Hartwig and Suzuki-Miyaura Reaction Yield Prediction [0.0]
  The model consists of a pre-trained bidirectional transformer-based encoder (BERT) and a multi-layer perceptron (MLP) with a regression head to predict the yield. We tested the model's performance on out-of-sample dataset splits of Buchwald-Hartwig and achieved comparable results with the state-of-the-art.
  arXiv  Detail & Related papers  (2022-04-27T07:28:27Z)
• SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning [168.89470249446023]
  We present SURF, a semi-supervised reward learning framework that utilizes a large amount of unlabeled samples with data augmentation. In order to leverage unlabeled samples for reward learning, we infer pseudo-labels of the unlabeled samples based on the confidence of the preference predictor. Our experiments demonstrate that our approach significantly improves the feedback-efficiency of the preference-based method on a variety of locomotion and robotic manipulation tasks.
  arXiv  Detail & Related papers  (2022-03-18T16:50:38Z)
• Active Learning-Based Optimization of Scientific Experimental Design [1.9705094859539976]
  Active learning (AL) is a machine learning algorithm that can achieve greater accuracy with fewer labeled training instances. This article performs a retrospective study on a drug response dataset using the proposed AL scheme. It shows that scientific experimental design, instead of being manually set, can be optimized by AL.
  arXiv  Detail & Related papers  (2021-12-29T20:02:35Z)
• SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation [72.58308581812149]
  We propose an effective approach, dubbed SelectAugment, to select samples to be augmented in a deterministic and online manner. Specifically, in each batch, we first determine the augmentation ratio, and then decide whether to augment each training sample under this ratio. In this way, the negative effects of the randomness in selecting samples to augment can be effectively alleviated and the effectiveness of DA is improved.
  arXiv  Detail & Related papers  (2021-12-06T08:38:38Z)
• Jo-SRC: A Contrastive Approach for Combating Noisy Labels [58.867237220886885]
  We propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency) Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution.
  arXiv  Detail & Related papers  (2021-03-24T07:26:07Z)
• Chemical Property Prediction Under Experimental Biases [26.407895054724452]
  This study focuses on mitigating bias in the experimental datasets. We adopted two techniques from causal inference combined with graph neural networks that can represent molecular structures. The experimental results in four possible bias scenarios indicated that the inverse propensity scoring-based method and the counter-factual regression-based method made solid improvements.
  arXiv  Detail & Related papers  (2020-09-18T08:40:57Z)

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.