setup task dataset template

app.py

@@ -1,3 +1,6 @@
+# from data.single_pred_tasks.tasks import _TASKS
+import task_datasets as data
+
 from flask import Flask, flash, redirect, render_template, request, session
 
 app = Flask(__name__)
@@ -6,6 +9,41 @@
 def index():
     return render_template('index.html')
 
+@app.route('/start')
+def start():
+    return render_template('start.html')
+
+@app.route('/overview')
+def overview():
+    return render_template('overview/index.html')
+
+@app.route('/single_pred_tasks/overview')
+def single_pred_tasks():
+    vars = {
+        "items": data.single_pred_tasks.tasks,
+        "data": [(endpt, label, data.single_pred_tasks.datasets[endpt]) for endpt, label, _ in data.single_pred_tasks.tasks[1:]] # skip adme 
+    }
+    return render_template('single_pred_tasks/index.html', **vars)
+
+@app.route("/single_pred_tasks/<task>")
+def single_pred_tasks_data(task):
+    task = task.lower()
+    if task == "adme":
+        return render_template("single_pred_tasks/adme.html")
+    elif task in data.single_pred_tasks.datasets:
+        datasets = []
+        for var in data.single_pred_tasks.datasets[task]:
+            if var in data.single_pred_tasks.meta:
+                if len(data.single_pred_tasks.meta[var]) != 10:
+                    raise Exception(var, len(data.single_pred_tasks.meta[var])) 
+                datasets.append([var] + data.single_pred_tasks.meta[var])
+        args = {
+            "datasets": datasets,
+            "task": task,
+        }
+        return render_template("single_pred_tasks/task.html", **args)
+    return redirect("/single_pred_tasks/overview")
+
 
 if __name__ == '__main__':
     app.run(debug=True)  # debug=True for development mode

scripts/app.js

@@ -0,0 +1,37 @@
+document.addEventListener('DOMContentLoaded', () => {
+
+  // Get all "navbar-burger" elements
+  const $navbarBurgers = Array.prototype.slice.call(document.querySelectorAll('.navbar-burger'), 0);
+
+  // Check if there are any navbar burgers
+  if ($navbarBurgers.length > 0) {
+
+    // Add a click event on each of them
+    $navbarBurgers.forEach( el => {
+      el.addEventListener('click', () => {
+
+        // Get the target from the "data-target" attribute
+        const target = el.dataset.target;
+        const $target = document.getElementById(target);
+
+        // Toggle the "is-active" class on both the "navbar-burger" and the "navbar-menu"
+        el.classList.toggle('is-active');
+        $target.classList.toggle('is-active');
+
+      });
+    });
+  }
+
+});
+
+$(document).ready(function() {
+  $('#tabs li').on('click', function() {
+    var tab = $(this).data('tab');
+
+    $('#tabs li').removeClass('is-active');
+    $(this).addClass('is-active');
+
+    $('#tab-content p').removeClass('is-active');
+    $('p[data-content="' + tab + '"]').addClass('is-active');
+  });
+});

task_datasets/__init__.py

@@ -0,0 +1,3 @@
+from .single_pred_tasks import SinglePredTasks
+
+single_pred_tasks = SinglePredTasks()

task_datasets/single_pred_tasks/__init__.py

@@ -0,0 +1,10 @@
+
+from .tasks import _TASKS
+from .datasets import _DATASETS, _META
+
+class SinglePredTasks:
+    def __init__(self):
+        self.tasks = _TASKS
+        self.datasets = _DATASETS
+        self.meta = _META
+

task_datasets/single_pred_tasks/datasets.py

@@ -0,0 +1,192 @@
+_DATASETS = {
+    "tox": ["hERG", "hERG_Karim", "AMES", "DILI", "Skin Reaction", "LD50_Zhu", "Carcinogens_Lagunin", "ToxCast", "Tox21", "ClinTox"],
+    "hts": ["HIV", "SARSCoV2_3CLPro_Diamond", "SARSCoV2_Vitro_Touret", "Orexin1 Receptor", "M1 Muscarinic Receptor Agonists", 
+            "M1 Muscarinic Receptor Antagonists", "Potassium Ion Channel Kir2.1", "KCNQ2 Potassium Channel", "Cav3 T-type Calcium Channels", 
+            "Choline Transporter", "Serine/Threonine Kinase 33", "Tyrosyl-DNA Phosphodiesterase"],
+    "qm": ["QM7b", "QM8", "QM9"],
+    "yields": ["Buchwald-Hartwig", "USPTO"],
+    "epitope": ["IEDB_Jespersen", "PDB_Jespersen"],
+    "develop": ["TAP", "SAbDab_Chen"],
+    "CRISPROutcome": ["Leenay"],
+}
+
+_META = {
+    "LD50_Zhu": [
+        "acute-toxicity-ld50", # id
+        "Acute Toxicity LD50", # full name
+        "Acute toxicity LD50 measures the most conservative \
+            dose that can lead to lethal adverse effects. The higher the dose, the more lethal of a drug. \
+                This dataset is kindly provided by the authors of [1].", # dataset description
+        "Regression. Given a drug SMILES string, predict its acute toxicity.",  # Task Description
+        " 7,385 drugs. ", # dataset statistics
+        ["Random Split", "Scaffold Split"], # split types
+        "Tox", # class name
+        [
+            [
+                "[1] Zhu, Hao, et al. “Quantitative structure− activity relationship modeling of rat acute toxicity \
+                by oral exposure.” Chemical research in toxicology 22.12 (2009): 1913-1921. ",
+                "https://pubs.acs.org/doi/abs/10.1021/tx900189p?casa_token=vfBbuxuUCqEAAAAA:YAcI0r4Z3rtlRYP_\
+                    l5H8OlTfdUh3DVlO6ws_h1NkhpaXH3-NrdI2-s5ghWWJbxfPQw-KhQIAwMi1Di3v"   
+            ],  # reference, reference link
+        ], # references
+        "CC BY 4.0.", # license
+        "https://creativecommons.org/licenses/by/4.0/", # license link
+    ],
+    "hERG": [
+        "herg-blockers",
+        "hERG blockers",
+        "Human ether-à-go-go related gene (hERG) is crucial for the coordination of the heart's beating. Thus, if \
+            a drug blocks the hERG, it could lead to severe adverse effects. Therefore, reliable prediction of hERG \
+                liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related \
+                    attritions in the later development stages. ",
+        "Binary classification. Given a drug SMILES string, predict whether it blocks (1) or not blocks (0).",
+        "648 drugs.",
+        ["Random Split", "Scaffold Split"],
+        "Tox",
+        [
+            [
+                "[1] Wang, Shuangquan, et al. “ADMET evaluation in drug discovery. 16. Predicting hERG blockers by combining \
+                multiple pharmacophores and machine learning approaches.” Molecular Pharmaceutics 13.8 (2016): 2855-2866.",
+                "https://pubs.acs.org/doi/abs/10.1021/acs.molpharmaceut.6b00471?casa_token=YszQx1sl0QgAAAAA:lIztZjmLzi3CPjnYh4lVFe3FAjOKF12N9IwBxjV4wiGw6S6QbpnegfgHEjwPzbgmpW-Bk9VvY9RLAsVo"
+            ],
+        ],
+        "CC BY 4.0.",
+        "https://creativecommons.org/licenses/by/4.0/",
+    ],
+    "hERG_Karim": [
+        "herg-karim-et-al",
+        "hERG Karim et al.",
+        "A integrated Ether-a-go-go-related gene (hERG) dataset consisting of molecular structures\
+            labelled as hERG (<10uM) and non-hERG (>=10uM) blockers in the form of SMILES strings was obtained from the\
+                DeepHIT, the BindingDB database, ChEMBL bioactivity database, and other literature.",
+        "Binary classification. Given a drug SMILES string, predict whether it blocks (1, <10uM) or not blocks (0, >=10uM).",
+        "13,445 drugs. ",
+        ["Random Split", "Scaffold Split"], 
+        "Tox",
+        [
+            [
+                "[1] Karim, A., et al. CardioTox net: a robust predictor for hERG channel blockade based on deep learning \
+                meta-feature ensembles. J Cheminform 13, 60 (2021).",
+                "https://doi.org/10.1186/s13321-021-00541-z"   
+            ]
+        ],
+        "CC BY 4.0.",
+        "https://creativecommons.org/licenses/by/4.0/"
+    ],
+    "AMES": [
+        "ames-mutagenicity",
+        "AMES Mutagenicity",
+        "Mutagenicity means the ability of a drug to induce genetic alterations. Drugs that can cause damage to\
+            the DNA can result in cell death or other severe adverse effects. Nowadays, the most widely used assay for\
+                testing the mutagenicity of compounds is the Ames experiment which was invented by a professor named Ames.\
+                    The Ames test is a short-term bacterial reverse mutation assay detecting a large number of compounds which\
+                        can induce genetic damage and frameshift mutations. The dataset is aggregated from four papers",
+        "Binary classification. Given a drug SMILES string, predict whether it is mutagenic (1) or not mutagenic (0).",
+        "7,255 drugs.",
+        ["Random Split", "Scaffold Split"], 
+        "Tox",
+        [
+            [
+                "[1] Xu, Congying, et al. “In silico prediction of chemical Ames mutagenicity.” Journal of chemical information \
+                and modeling 52.11 (2012): 2840-2847.",
+                "https://pubs.acs.org/doi/abs/10.1021/ci300400a?casa_token=A86ksblef0kAAAAA:2kLxP4j2NOigeBsSqUb8C9ThZLVBR_\
+                    Ztc8gJg_HhyLCRtZF-MfMMyq4bIwhMlH0MyJXZuWkFXXrGqiMR"  
+            ]
+        ],
+        "CC BY 4.0.",
+        "https://creativecommons.org/licenses/by/4.0/"
+    ],
+    "DILI": [
+        "dili-drug-induced-liver-injury",
+        "DILI (Drug Induced Liver Injury)",
+        "Drug-induced liver injury (DILI) is fatal liver disease caused by drugs and it has been the single most frequent cause\
+            of safety-related drug marketing withdrawals for the past 50 years (e.g. iproniazid, ticrynafen, benoxaprofen).\
+                This dataset is aggregated from U.S. FDA’s National Center for Toxicological Research. ",
+        "Binary classification. Given a drug SMILES string, predict whether it can cause liver injury (1) or not (0).",
+        "475 drugs",
+        ["Random Split", "Scaffold Split"],
+        "Tox",
+        [
+            [
+                "[1] Xu, Youjun, et al. “Deep learning for drug-induced liver injury.” Journal of chemical information and \
+                modeling 55.10 (2015): 2085-2093. ",
+                "https://pubs.acs.org/doi/abs/10.1021/acs.jcim.5b00238" 
+            ],
+        ],
+        "CC BY 4.0.",
+        "https://creativecommons.org/licenses/by/4.0/"
+    ],
+    "Skin Reaction": [
+        "skin-reaction",
+        "Skin Reaction",
+        "Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals \
+            that leads to skin sensitization. The dataset used in this study was retrieved from the ICCVAM (Interagency \
+                Coordinating Committee on the Validation of Alternative Methods) report on the rLLNA.",
+        "Binary classification. Given a drug SMILES string, predict whether it can cause skin reaction (1) or not (0). ",
+        "404 drugs.",
+        ["Random Split", "Scaffold Split"],
+        "Tox", 
+        [
+            [
+                "[1] Alves, Vinicius M., et al. “Predicting chemically-induced skin reactions. Part I: QSAR models\
+                of skin sensitization and their application to identify potentially hazardous compounds.” Toxicology\
+                    and applied pharmacology 284.2 (2015): 262-272. ",
+                "https://www.sciencedirect.com/science/article/pii/S0041008X14004529?casa_token=gMz283g8Tj0AAAAA:\
+                    DNKlQvZC2fUfMNz4vbqB7WEQGSDMpibtoST_G8vhZ7I4GK950VKxrLTK3jBrSlYKH5flC4sJ6O4"
+
+            ],
+            [
+                "[2] The reduced murine local lymph node assay: an alternative test method using fewer animals to assess\
+                the allergic contact dermatitis potential of chemicals and products. ",
+                "https://ntp.niehs.nih.gov/whatwestudy/niceatm/test-method-evaluations/immunotoxicity/llna/index.html"
+            ],
+        ],
+        "CC BY 4.0.",
+        "https://creativecommons.org/licenses/by/4.0/" 
+    ],
+    "Carcinogens_Lagunin": [
+        "carcinogens",
+        "Carcinogens",
+        "A carcinogen is any substance, radionuclide, or radiation that promotes carcinogenesis, the formation of cancer.\
+            This may be due to the ability to damage the genome or to the disruption of cellular metabolic processes.",
+        "Binary classification. Given a drug SMILES string, predict whether it can cause carcinogen.",
+        "278 drugs.",
+        ["Random Split", "Scaffold Split"],
+        "Tox",
+        [
+            [
+                "[1] Lagunin, Alexey, et al. “Computer‐aided prediction of rodent carcinogenicity by PASS and CISOC‐PSCT.”\
+                QSAR & Combinatorial Science 28.8 (2009): 806-810. ",
+                "https://onlinelibrary.wiley.com/doi/abs/10.1002/qsar.200860192?casa_token=NR55Na01l8gAAAAA:603Kr5drd\
+                    -EIWlXFQVJpnpEm05-GxMBwVeBJiORBah8IIsPt1yvy8UNdADagv1ty8SlPvX4XF4r7QpWK" 
+            ],
+            [
+                "[2] Cheng, Feixiong, et al. “admetSAR: a comprehensive source and free tool for assessment of chemical\
+                ADMET properties.” (2012): 3099-3105.",
+                "https://pubs.acs.org/doi/abs/10.1021/ci300367a?casa_token=9fEKYcMw5zoAAAAA:ZDPZ-e-M9RulemgBjAPNkSn\
+                    WXlVCcPzkxNK9bX40Abz9o24NpitsXY0tizgDL5ekPiNtEPFSeOwwUTHG"
+            ],
+        ],
+        "CC BY 4.0.",
+        "https://creativecommons.org/licenses/by/4.0/"
+    ],
+    "ClinTox": [
+        "clintox",
+        "ClinTox",
+        "The ClinTox dataset includes drugs that have failed clinical trials for toxicity reasons and also drugs\
+            that are associated with successful trials.",
+        "Binary classification. Given a drug SMILES string, predict the clinical toxicity. ",
+        "1,484 drugs. ",
+        ["Random Split", "Scaffold Split"],
+        "Tox",
+        [
+            [
+                "[1] Gayvert, Kaitlyn M., Neel S. Madhukar, and Olivier Elemento. “A data-driven approach to predicting \
+                successes and failures of clinical trials.” Cell chemical biology 23.10 (2016): 1294-1301.",
+                "https://pubmed.ncbi.nlm.nih.gov/27642066/"
+            ]
+        ],
+        "CC BY 4.0.",
+        "https://creativecommons.org/licenses/by/4.0/"
+    ]
+}

task_datasets/single_pred_tasks/tasks.py

@@ -0,0 +1,10 @@
+_TASKS = [
+    ["adme", "ADME", "Pharmaco-kinetics"],
+    ["tox", "Tox", "Toxicity"],
+    ["hts", "HTS", "High-Throughput Screening"],
+    ["qm", "QM", "Quantum Mechanics"],
+    ["yields", "Yields", "Reaction Yields Prediction"],
+    ["epitope", "Epitope", "Epitope Prediction"],
+    ["develop", "Develop", "Developability Prediction"],
+    ["CRISPROutcome", "CRISPROutcome", "CRISPR Repair Prediction"],
+]

templates/index.html

@@ -55,15 +55,15 @@
 
 
 
-                    <a href="/start/" class="navbar-item ">Start</a>
+                    <a href="/start" class="navbar-item ">Start</a>
 
 
 
                     <div class="navbar-item has-dropdown is-hoverable">
-                        <a href="/overview/" class="navbar-link ">Datasets</a>
+                        <a href="/overview" class="navbar-link ">Datasets</a>
                         <div class="navbar-dropdown">
 
-                            <a href="/overview/" class="navbar-item ">Overview</a>
+                            <a href="/overview" class="navbar-item ">Overview</a>
 
                             <a href="/single_pred_tasks/overview" class="navbar-item ">Single-instance Prediction</a>
 
@@ -96,10 +96,10 @@
 
 
                     <div class="navbar-item has-dropdown is-hoverable">
-                        <a href="/benchmark/overview/" class="navbar-link ">Leaderboards</a>
+                        <a href="/benchmark/overview" class="navbar-link ">Leaderboards</a>
                         <div class="navbar-dropdown">
 
-                            <a href="/benchmark/overview/" class="navbar-item ">Guidelines</a>
+                            <a href="/benchmark/overview" class="navbar-item ">Guidelines</a>
 
                             <a href="/benchmark/admet_group/overview" class="navbar-item ">ADMET Group</a>
 
@@ -154,7 +154,7 @@
             <div class="container is-pulled-left">
 		        <div class="columns">
 
-		            <div class="column has-text-centered is-pulled-left"> <a href="/start/" class="button is-rounded is-large is-info is-focused">Get Started</a></div>
+		            <div class="column has-text-centered is-pulled-left"> <a href="/start" class="button is-rounded is-large is-info is-focused">Get Started</a></div>
 
 		            <div class="column has-text-centered is-pulled-left"><a href="https://twitter.com/ProjectTDC" class="button is-rounded is-large is-primary is-focused">Follow us on Twitter</a></div>
 		            <div class="column has-text-centered is-pulled-left"><a href="https://join.slack.com/t/pytdc/shared_invite/zt-x0ujg5v6-zwtQZt83fhRdgrYjXRFz5g" class="button is-rounded is-large is-primary is-focused">Join us on Slack</a></div>
@@ -431,7 +431,7 @@
 <div class="container">
         <div class="columns is-vcentered">
             <div class="column is-10 is-offset-1 has-text-centered">
-                <a href="/start/" class="button is-rounded is-large is-info is-focused">Get Started</a>
+                <a href="/start" class="button is-rounded is-large is-info is-focused">Get Started</a>
             </div>
         </div>
 </div>
@@ -482,6 +482,5 @@
     </div>
 </footer>
 
-    <script src="/assets/js/app.js" type="text/javascript"></script><!-- footer scripts --></body>
 </html>