A deep learning and digital archaeology approach for mosquito repellent discovery
∗
Denotes equal contribution † Corresponding authors.
Abstract
Insect-borne diseases kill >0.5 million people annually.
Currently available repellents for personal or household protection are limited in their efﬁcacy, applicability, and safety proﬁle.
Here, we describe a machine-learning-driven highthroughput method for the discovery of novel repellent molecules.
To achieve this, we digitized a large, historic dataset containing 19,000 mosquito repellency measurements.
We then trained a graph neural network (GNN) to map molecular structure and repellency.
We applied this model to select 317 candidate molecules to test in parallelizable behavioral assays, quantifying repellency in multiple pest species and in follow-up trials with human volunteers.
The GNN approach outperformed a chemoinformatic model and produced a hit rate that increased with training data size, suggesting that both model innovation and novel data collection were integral to predictive accuracy.
We identiﬁed >10 molecules with repellency similar to or greater than the most widely used repellents.
This approach enables computational screening of billions of possible molecules to identify empirically tractable numbers of candidate repellents, leading to accelerated progress towards solving a global health challenge.
Introduction
Mosquitos and other blood-sucking arthropods carry and transmit diseases that kill hundreds of thousands of people each year[33, 2].
To make continued progress on this global health issue, we must discover, manufacture, and deploy more efﬁcient molecules for pest control across a variety of application spaces collectively termed vector control; this includes molecules that affect life history traits, such as insecticides, and molecules that affect host-seeking behavior, e.g. topical repellents for personal protection and spatial repellents applied to a home or room.
Commonly used repellents such as DEET (N,N-diethyl-meta-toluamide), Picaridin (Hydroxyethyl isobutyl piperidine carboxylate), and IR3535 (Ethyl butylacetylaminopropionate) require high concentrations of over 40% [1] which limit their use to topical applications.
Furthermore, they have undesirable properties and/or safety proﬁles; for example, DEET is a plasticizer, precluding its use on synthetic clothing or shelter surfaces, and it is toxic to some vertebrate wildlife[1].
Some commonly used repellents are speciesspeciﬁc; for example IR3535 is effective against Aedes aegypti but is ineffective against Anopheles mosquitoes and is therefore not recommended for use in malaria-endemic regions.
Over the past few decades, only a few dozen new repellent molecule candidates have been found and very few have reached the market; an approach to rapidly discover and validate large numbers of new candidates is desperately needed.
Multiple strategies exist for identifying insect repellent candidates.
Behavioral assays seek to directly test repellent activity in realistic conditions.
Recognizing the devastating effect of insectborne diseases (including dengue fever) faced by the United States Army during the second world war, the U.S. Department of Agriculture (USDA) tested 30,000 molecules for their effectiveness as repellents and insects on mosquitos, ticks, and other insect species[18, 36].
In particular, 14, molecules were tested for their effectiveness as mosquito (A. aegypti and A. quadrimaculatus) repellents using human volunteers; this effort led to the discovery of DEET.
Structure-targeted modeling of the obligatory insect olfactory co-receptor Orco led to discovery of picaridin[6] and VUAA1[21].
Scaffold-hopping techniques[35] can focus the molecular search space, and in combination with arm-in-cage testing, led to the discovery of IR3535[26] and DEPA[22].
Chemoreceptor studies exploit the molecular mechanism of action: DEET and IR3535 modulate the activity of odorant and gustatory receptors[12, 13] but may also affect cholinergic signaling[3, 28].
The exact molecular details of their mode of action are not fully understood, and may be very species-speciﬁc (Aﬁfy and Potter, 2019[4]).
It is difﬁcult to more broadly and systematically explore molecular space using each of these approaches, as they can be labor-intensive.
The USDA dataset represents a wealth of information on the relationship between molecular structure and arthropod behavior.
Small parts of this dataset have been used previously to train computational models of mosquito repellency[38, 23, 5], typically on speciﬁc structural families of molecules.
Katritzky et al.[24] used an artiﬁcial neural network model trained on 167 carboxamides and found 1 carboxamide candidate with high repellency activity.
As modern deep learning models show performance which scales in proportion to the volume of their training data[19], we hypothesized that exploiting the f ull size of the USDA dataset would provide a strong starting point for a new deep learning model.
We selected a graph neural network architecture (GNN), as GNNs have been shown to have superior performance to computable chemoinformatics descriptors in predicting the properties of a molecule from its chemical structure, given a sufﬁciently large dataset[39, 16].
Notably, previous work demonstrated that a GNN-based human odor model outperforms standard cheminformatics models even on insect behavior datasets.[38, 23, 5]
Here we present a data-driven workﬂow for the discovery and validation of novel molecules for behavioral modiﬁcation in arthropods.
The critical components underlying the success of this approach are 1) expanded training data made possible by a complete digitization of the USDA dataset; 2) high-quality validation data using a parallelizable membrane-feeding assay that does not require human volunteers; and 3) a graph neural network model to learn the relationship between molecular structure and these data.
We iteratively use this model to propose candidates from a purchasable chemical library, validate these candidates for repellency, and use these results to expand the training dataset and therefore improve the predictive accuracy of the behavior model (Figure 1).
Through this process we have discovered a chemically diverse set of molecules with effectiveness equal to or greater than DEET, unlocking new potential capabilities in vector control.
Results 2.1 Digitizing a rich historical dataset
The USDA dataset is unmatched in size and scope, but for decades existed only in print.
Google
Books scanned and made available the original work online[18], and for this work we subsequently converted it into a machine-readable format.
After some preprocessing to make the dataset easier to read, we employed expert curators to transcribe the full records and provide canonical structures for each listed molecule (Fig.
2A, Methods).
We then focused our analysis on the four mosquito repellency assays contained in this dataset: two mosquito species, Aedes aegypti and Anopheles quadrimaculatus; and two repellency contexts, skin and cloth.
Together these comprise 19, labeled data points on repellency of speciﬁc molecules (Fig.
2B), representing a broad range of structural and functional classes (Fig.
2C).
This large dataset served as training data for our modeling efforts.
2.2 Assessment of repellent candidates
In order to test model predictions and iteratively expand the training data, we adapted a standard membrane feeding assay (SMFA), commonly used in malaria research[7, 10], to evaluate the repellency against Anopheles stephensi mosquitoes. Repellency was evaluated by prevention of blood feeding relative to a vehicle (ethanol) control (Fig. 2D).
The assay was used to evaluate each molecules potency and duration of effect as exempliﬁed for the reference molecule DEET in Fig 2E.
We assessed the inherent inter-assay reliability by comparing repellency levels for a diverse set of molecules from independent experiments (tested at 25 ¸tg/cm2, r=0.81, Fig. 2F).
Using a cut-off of 75% repellency as measured 120 min after initial application, selected to include widely used repellents (e.g. DEET, dimethyl phthalate, and indalone), approximately 3/4 of the molecules classiﬁed as active in a ﬁrst assay were conﬁrmed to be active upon re-testing.
The USDA dataset was collected 70 years ago using arm-in-cage experiments, involving human volunteers, while our assay was conducted with a surrogate target.
We evaluated the relationship between these two experiments by directly comparing the activity of 38 molecules with their repellency reported in the USDA dataset.
We found considerable concordance between the historical
USDA dataset and the membrane feeding assays (p<0.01 Mann Whitney U test, Fig.
2G) , despite differences in experimental setup.
However, some disagreement was observed, highlighting the need for additional data collection.
2.3 Modeling mosquito repellency behavior
Using the USDA dataset, we sought to create a representation of molecules speciﬁc to mosquito repellency behavior.
It has been previously demonstrated that graph neural networks (GNNs) are particularly adept at creating task-speciﬁc representations[15, 39], and that representational power extends to the domain of olfaction[31, 30].
We trained GNN models on the USDA dataset, observing an AUC=0.881 on the cloth-Aedes aegypti task, the task with the largest dataset (Methods).
We then use the output heads from the ensemble models on all four USDA tasks to create the USDA learned representation (USDALR, Figure 1).
percentage: p<0.05 (Z-test of proportions)
We sought to build a model that was speciﬁc for the activity behavior in our membrane feeder assay.
We created an assay model by ﬁrst using the ﬁxed USDA learned representation to embed input molecules, then adding a two layer, 256-node neural network to learn to predict the assay data.
We applied the assay model to make predictions on novel repellent candidate compounds from a large library of purchasable molecules provided by the vendor eMolecules[17].
We ﬁltered this library for desirable qualities such as volatility and low cost, and we further screened out molecules which did not pass an inhalation toxicity ﬁlter (Methods).
From among those compounds passing these ﬁlters (˜10k molecules), we selected those which had sufﬁcient predicted repellency and–to ensure novelty–which were structurally distinct (Tanimoto similarity <0.8) from those in the USDA dataset or previous candidate selections.
Assay results from each batch of selections were added to the assay dataset; for each subsequent batch of selections, the assay model was re-trained on the expanded assay dataset.
Detailed notes on the speciﬁc modeling setup for each batch are located in the Supplementary section.
Over several iterations, a total of 400 molecules were purchased and further screened empirically according to a solubility criterion (Methods); those that passed (n=317) were then tested for repellency with the membrane-feeder assay.
Over the course of selections spanning over a year, some adjustments were made to both the USDA model and the membrane-feeder assay.
In particular, our hit deﬁnition evolved with our dataset size and model capability: we initially deﬁned a hit as 90% repellency using a dose of 25 ¸tg/cm2 as measured at T=2min (1 measurement), but in the ﬁnal batch of selections, we changed our deﬁnition to 75% repellency as measured at T=120min ( measurements).
2.4 The hit rate improves with training data size
To evaluate the contribution of the training data to our performance, we retrospectively scored high-repellency candidates in two phases: before the USDA dataset was available (pre-USDA) and after we began using the USDA dataset to build and deploy the USDA learned representation (post-USDA).
In the pre-USDA phase, instead of using the USDA learned representation to embed molecules, we employed an odor-speciﬁc representation previously demonstrated to outperform standard cheminformatics representations on olfaction related tasks[30].
Further, at that time, we only had assay data for 34 molecules, so we opted to use a k-nearest neighbors model (k=10) to model assay activity.
In the post-USDA phase, the assay dataset size for the ﬁrst batch wasmolecules, and grew to a size of 402 molecules for our ﬁnal batch of selections (Supplemental Batch
Notes).
This large dataset made a huge difference; hit rates post-USDA measured on repellency time=2min increased to 49% from the pre-USDA level of only 29% (Figure 3A).
When we then raised the bar for hit classiﬁcation to require a longer duration of effect, hit rates dropped to 6% for predictions from the post-UDSA phase and 3% for predictions from the pre-USDA phase.
It is important to note that only the last batch in the post-USDA phase was trained to ﬁnd candidates meeting this new repellent standard; further iterations may have continued to improve performance as they did under the previous standard.
This hit rate comparison across the two different experimental phases aggregates changes in both representational approach and assay dataset size; how much did the USDA learned representation speciﬁcally, and by extension the USDA dataset, improve our models performance?
To estimate the contributions of the USDA representation, we performed a retrospective analysis comparing the USDA representation against two other chemical representation approaches: a cheminformatics representation (using Mordred descriptors[29]) and the odor-based representation[30] used in the pre-USDA phase.
The same assay model architecture was used for the different representations.
We split the full assay dataset into two parts, a training set composed of molecules from all batches of tests performed before the use of the USDA dataset (88 measurements) and an evaluation set of all molecules selected in the post-USDA phase (170 measurements).
We observed that the USDA learned representation model signiﬁcantly outperformed both alternatives on this prediction task (Figure 3B; USDA model AUC=0.74 [0.68,0.81]; Chemoinformatics model AUC=0.59 [0.50,0.67]; GNN Odor model AUC=0.60 [0.51,0.67]), suggesting that the historical dataset played a signiﬁcant role in the elevated predictive performance.
There is a selection bias because the selection of molecules for evaluation was done by the assay model using USDA learned representations.
One effect of this bias is that it reduces the expected number of negative examples, reducing the contrast between predicted repellents and non-repellents, resulting in a negative bias into all AUC measurements.
However, the model used for selection should suffer the greatest negative bias, suggesting that the performance difference we observed is an underestimate of the true advantage that the USDA model has over its alternatives, as would have been observed under a counterfactual unbiased selection of repellent [-candidates.2.
-]candidates. 2.5 Selected
hit molecules are chemically diverse
Training a model on a large pool of data containing a variety of molecules allows the model to generalize to larger areas of chemical space.
Figure 4 shows the distribution of molecules selected by our post-USDA models, and compares them to the active molecules reported in the USDA dataset itself.
The candidate selections made by our model explore some of the same regions of the USDA dataset, but ﬁnd hits in some underexplored regions of the original dataset (Figure 4A).
The ML- selected molecules were required to be a minimum of 0.2 Tanimoto distance from USDA molecules; we observe an overall median Tanimoto distance of 0.52 from USDA molecules across all of our selections, and a median distance of 0.48 from USDA molecules amongst active molecules (Figure 4B).
Using ClassyFire[14] to annotate each molecule, we found that molecules selected by our model are enriched in benzenoids, ethers, carboxylic acid derivatives and organoheterocyclic molecules when compared to the molecules measured by the USDA dataset (Figure 4C).
2.6 Top candidates show strong repellency in additional applications
While the membrane feeder assay provides a rapid measurement of repellency effectiveness, for realworld applications it is necessary to consider the effect of odorants released by human skin.
To assess repellency of hit molecules in the context of host skin emanations, we tested a representative set of our molecules in arm-by-cage experiments (Fig. 5A).
To this end, we selected 31 hit molecules that showed 75% repellency at a density of 25 ¸tg/cm2 at T=120 minutes at least once in the membrane feeder experiments, and 4 molecules with lower repellency activities.
When tested at a density of 13 ¸tg/cm2 in the arm-by-cage experiments, 43% of the tested molecules perform very well (75% repellency) and 67% of those even outperform DEET (>84% repellency) (Fig.
5B).
Overall, we observed high correspondence between repellency as measured in the feeder vs. the arm-in-cage assays (r=0.64), with 83% of hits from the former also reaching the hit threshold in the latter (Fig. 5C).
Our primary assay assessed repellency against A. stephensi, but other pest species also carry disease, and there are some known species-speciﬁc differences in repellency of known molecules (e.g. IR3535).
To address this concern, we selected 16 molecules based on their activity against
A. stephensi, 9 strong and 7 weak repellents.
We then used the original assay to test them against
A. aegypti and a modiﬁed assay (Fig.
5D) to test against I. scapularis, the black-legged tick.
We observed signiﬁcant generalization across pest species: 8 of the strong repellents (88%) demonstrated good repellency (>50% repellency) at 25 ¸tg/cm2 against A. aegypti, and 12 (75%) molecules were active (>75% repellency) at 540 ¸tg/cm2 against I. scapularis (ED50 of DEET 120 ¸tg/cm2, Fig.
5E).
Discussion
We developed and validated novel methods for identifying potential repellent molecules for vector control of deadly human and animal diseases.
First, we digitized a historic dataset rich with an unprecedented volume of relevant repellency data covering thousands of molecules.
Second, we applied and reﬁned a deep learning model architecture to learn the mapping between molecular structure and repellency in this dataset.
Third, we used a high-throughput experimental assay to prospectively validate predictions from this model, and to conduct active learning to iteratively improve model predictions.
Finally, we showed that these predictions identify new repellent candidates in underexplored regions of chemical space, and that some of these molecules show applicability across real-life context and across pest species.
This represents a promising approach to identify next-generation repellents and help solve one of humanitys greatest global health challenges.
Despite containing a surprisingly large quantity of relevant repellency data, the USDA dataset has remained underused, garnering only 200 citations in the last 50 years.
This surely stemmed in part from the limited visibility and accessibility of the data during most of this period, where it was accessible only via paper handbooks in physical libraries.
The Google Books digitization project scanned these handbooks, making images of the data visible to anyone with an internet connection.
However, many of the chemical names contained there-in were archaic or ambiguous, and so could not be effortlessly mapped to chemical structures; the repellency values themselves were also not machine readable.
The manual curation and digitization that we performed was the last step to unlock the power of these historical records.
The general pattern of connecting diffuse experimental records to support larger modeling efforts and meta-analyses continues to bear fruit[32, 37].
How important were these data?
Machine learning is data-driven, and frequently suffers from cold start problems; deep learning models are especially data-hungry, and ﬁnding enough data to train them to state-of-the-art performance can be a major challenge.
The USDA dataset solved this problem by allowing us to train a draft model, which we were then able to build upon using data from a modern experimental assay.
Several previous efforts to identify new repellents using machine learning have used only several dozen similar molecules to train their models [38, 23, 5, 8].
A larger slice of the historical dataset ( 2000 molecules) has been used to train a neural network model to both predict repellency and verify the repellency of known repellents [11].
Recently, larger datasets are becoming available for receptor-targeted QSAR (RT-QSAR)[27, 9], but until this current work, no machine-readable large-scale datasets have been available for BT-QSAR.
Most previous publications validated their repellency models only retrospectively by predicting the activity of known repellents, rather than prospectively[25] by using the model to identify new molecules with repellency behavior.
This typically leads to overestimation of predictive performance of new repellent candidates.
By contrast, we collected assay data for prospective validation of the model, and further used this data in an active learning loop to reﬁne the model, showing continued improvement in predictive performance as new data was collected.
Prospective validation has been used in the past to discover new repellent molecules: Picaridin was discovered at Bayer using pharmacophore modeling6, and a small set of acylpiperdines were discovered using neural networks trained on a small subset of USDA data[5].
However, these novel repellents have typically been structural near-neighbors of existing repellents.
By contrast, our model-selected candidates cover a much wider range of structural classes than previous repellency discovery attempts, facilitating our discovery of molecules with repellency activity greater than DEET even at 2 hours after application, and a subset that have repellency efﬁcacy when tested in the presence of attractive human skin emanations.
Machine learning, and particularly deep learning, is yielding impressive advances in applications in chemistry.
Several academic and industrial groups have used deep learning models to screen for new molecules with desirable properties, such as antibiotic activity or protein binding afﬁnity[27, 34, 40, 20].
The methods outlined in this paper can also be applied to other disease vectors, other classes of behavior-modifying molecules, and more broadly to enable hit discovery in arbitrary chemical applications.
Future work will be required to impose additional ﬁlters or modeling steps to satisfy additional criteria related to safety, biodegradability, odor, and skin-feel, in conjunction with experimental data about these important factors. Author Contributions
JNW, DMA, KMG, ABW curated and digitized the USDA dataset; JNW, BKL performed data cleaning and spotchecking of the dataset.
MV and KJD designed the mosquito assay and tick assay experiments; MV, LB, MWV, and RWMH performed the mosquito assay experiments; MV and MWV performed tick assay experiments. JNW designed the models with assistance from BS-L, BKL, and WWQ. JNW, MV, BS-L, RCG performed data analysis. JNW, MV, RCG wrote the manuscript.
ABW and KJD conceived the project.
All authors contributed to editing the manuscript.
Acknowledgements
We wish to thank Laura Pelsen-Posthumus for technical assistance in mosquito rearing, and GeertJan van Gemert and Pascal Miesen for provision of mosquitoes.
We thank Hans Dautel for help with the design of the tick repellency assay.
The authors thank Lucy Colwell, James Thompson, Max
Bileschi, and David Belanger for critical reading of the manuscript.
We also thank Sameer Kulkarni for assistance with OCR processing and Jonathan Brecher for his insights on transcribing structures from historical chemical names.
We thank Jeff Riffel, Carlos Ruiz, Ben Adlam, Jasper Snoek, and the Cambridge Brain Research team for giving helpful feedback during our discussions.
Some of the ﬁgures were created with BioRender.com.
We also thank the Bill and Melinda Gates Foundation for their generous support of this work.
A Method A.1 Mosquitoes and ticks
Both Anopheles stephensi and Aedes aegypti mosquitoes were maintained on a 5% sugar solution in a 26 ˇrC environment with 80% humidity, according to standard rearing procedures.
Adult Ixodes scapularis ticks were maintained in a 26 ˇrC environment with 90% humidity.
Mosquito behavioral assays Before each membrane feeding assay, 10-20 female Anopheles stephensi or Aedes aegypti mosquitoes (3-5 days old) were transferred to a paper cup covered with mosquito netting.
The mosquitoes were denied access to their normal sugar solution 4-6 hours prior to the feeding assay. 30 ¸tl of test molecule, dissolved in ethanol, was pipetted on a piece of mosquito netting (3x3 cm) and allowed to dry.
To ensure a regular and standardized airﬂow over the samples, a gastronorm tray (¡ 200mm) equipped with a computer fan (80x80x25mm, 12V, 0.08A) was placed over the samples.
After a speciﬁed time of evaporation (e.g., 2 hours), the sample was placed on top of the cup containing the mosquitoes.
The cups were then placed under a row of glass membrane feeders containing a pre-warmed (37 ˇrC) blood meal.
The mosquitoes were allowed to feed for 15 minutes.
The number of fed and unfed mosquitoes were then recorded.
For the arm-by-cage assays, 30-50 female Anopheles stephensi mosquitoes were transferred to an acrylic cup (150x100mm) covered with mosquito netting.
1 mL of test molecule (0.5% w/v), dissolved in ethanol, was pipetted on a piece of cheesecloth (6x9 cm) and taped to an acrylic panel (6mm thick) with a cutout and allowed to dry.
A panel with an untreated piece of cloth was then placed next to the acrylic cups containing the mosquitoes and a volunteer placed his hand against the panel for 5 minutes.
The mosquitoes were ﬁlmed and the maximum number of mosquitoes landing simultaneously was recorded.
This was then repeated with a piece of treated cloth and the number of landings was normalized to the control, which is the ethanol solvent alone.
All arm-by-cage assays were designed and run by TropIQ.
A.2 Tick behavioral assays
The setup of the tick repellency assay is shown in ﬁgure 5D.
The assay consists of a heated (37žC) aluminum plate (235 x 235 mm) that is painted white.
Before the test, 750 ¸tl of test molecule, dissolved in ethanol, is pipetted on a ring of ﬁlter paper (OD = 150 mm, ID = 122 mm).
The ring is then transferred onto the heated plate and 5 Ixodes scapularis ticks are placed in the center.
The ticks are monitored for 5 minutes and the number of ticks that cross the ﬁlter paper are counted.
Repellency is expressed as the percentage of ticks that did not cross the ﬁlter paper.
A.3 Historical dataset preparation
The scanned versions of the USDA datasets, available from Google Books, were converted into a machine-readable format.
Chemical structures (Simpliﬁed Molecular-Input Line-Entry System, or
SMILES) 40 were assigned to each single molecule entry in the dataset.
The raw PDFs of the two repellency handbooks41,42 used to create the USDA dataset are available on Google Books.
For this study, the PDFs were converted to png ﬁles, then sliced by rows according to bounding boxes drawn by curators.
The row sliced images and the full page images were provided to a third-party curation service, who transcribed the chemical names as SMILES and corresponding assay results.
Post-processing analysis and evaluation of a random sample of 150 entries suggest an error rate of <5% in the chemical structures.
The ﬁnal dataset resulted in 18,886 data points on 14,187 molecules.
This includes the results on two assay setups, one testing the effectiveness of the candidates on cloth, the other on human skin, and also two different mosquito species (Aedes aegypti and Anopheles quadrimaculatus); all four combinations of these two species and conditions were used in this study.
USDA dataset labels in the source material were repellency ratings given as integers from 1 (worst) to 5 [-(best).
-](best).
A.4 USDA Dataset Modeling and Representation Learning
Each of the USDA tasks was split into a 70:15:15 train/validation/test split such that molecules were assigned to the same split across all tasks; in particular, if a molecule is in the training set for one task, it was also in the training split for the other tasks for which there was a measurement.
Molecules in the USDA dataset that were also used in the pre-USDA phase (Batches 1-3, see Supplementary
Batch notes) were excluded from the USDA training sets.
Iterative stratiﬁcation over the label classes across each task was applied to balance the labels in the training/validation/test splits for each task.
Graph neural network models (GNNs) were trained on each of the four mosquito repellent tasks from the USDA dataset.
Each model provided predicted probabilities of the class label and combination class labels; speciﬁcally, the model predicted the probability of the class label being: [1], [2], [3], [4], [5], [1 OR 3 OR 4 OR 5], [3 OR 4 OR 5], [1 OR 4 OR 5].
AUROC performance on the [3 OR 4 OR 5] label objective was used to optimize the models.
The graph neural network used message passing layers (MPNN44), with a max atom size of 45, 30 atom features, and 6 bond features.
Hyperparameter selections were made using the Vizier43 default Bayesian optimization algorithm over 300 trials.
The USDA learned representation was constructed from the outputs of the frozen ensemble model of the best 50 models from hyperparameters trained on the USDA dataset.
For the last batch of selections, the models used to create the ensemble model ranged in AUROC performance from 0.872 to 0.881.
A.5 Model Training on Membrane Feeding Assay Data
To train the models for activity in membrane feeding assays, assay results were binarized: a positive label for repellency activity was deﬁned as >90% at T=2min at 25 ¸tg/cm2, and >75% for T=120min.
For model evaluation and hyperparameter selection, the dataset was split into a 70:30 train/test split, using iterative stratiﬁcation to balance the label classes.
The model trained on the USDA dataset was used to generate specialized representations for the molecules.
A two-layer neural network model with 256 nodes was used to predict the binarized activity label given the molecule; the hyperparameters of this model were selected with grid search.
At inference time, to make predictions on new candidates, the model was retrained using the entire dataset.
A.6 Molecule Selection
We began by ﬁltering molecules listed in the eMolecules catalog – which contains 1 million commercially available molecules – for atom composition (C/N/O/S/H only), price (<$1000 per 10 grams), purity (>95%), and availability (<4 weeks lead time).
We utilized a toxicity ﬁlter to remove potentially harmful molecules, according to a toxicologist-recommended protocol.
In this protocol, we classiﬁed molecules by their mutagen / Cramer class using ToxTree, calculated their vapor pressure at room temperature, and then compared the likely exposure air volume to OSHA daily exposure limits for the corresponding toxicity class.
We removed likely odorless molecules according to water-soluble (cLogP < 0) and nonvolatile (boiling point > 300 C) criteria.
We manually removed molecules that were likely to degrade or react under our experimental conditions.
After training the assay model, molecules were selected such that they had a prediction score above an f1 optimized cutoff score, and then selected such that they had a Tanimoto similarity of <0.8 from other selected molecules and the USDA dataset.
A minimum solubility threshold of 10 mg/ml in absolute ethanol was used as a last criterion.
Molecules with an ethanol solubility below the threshold were abandoned.
Detailed selection criteria for batches are reported in the Supplemental section.