Explanation Information

ADMETlab 2.0: an integrated online platform for accurate and

comprehensive predictions of ADMET properties

Guo-Li Xiong, Zhen-Xing Wu, Jia-Cai Yi, Li Fu, Zhi-Jiang Yang, Chang-Yu Hsieh, Ming-Zhu Yin, Xiang-Xiang
Zeng, Cheng-Kun Wu, Ai-Ping Lu, Xiang Chen, Ting-Jun Hou*, Dong-Sheng Cao*

*Correspondence: oriental-cds@163.com, tingjunhou@zju.edu.cn

1. Physicochemical property

Molecular Weight
Contain hydrogen atoms. Optimal:100~600, based on Drug-Like Soft rule.

Volume
Van der Waals volume.

Density
Density = MW / Volume

nHA
Number of hydrogen bond acceptors. Sum of all O and N. Optimal: 0~12, based on Drug-Like Soft rule.

nHD
Number of hydrogen bond donors. Sum of all OHs and NHs. Optimal:0~7, based on Drug-Like Soft rule.

nRot
Number of rotatable bonds. In some situation Amide C-N bonds are not considered because of their high
rotational energy barrier. Optimal:0~11, based on Drug-Like Soft rule.

nRing
Number of rings. Smallest set of smallest rings. Optimal:0~6, based on Drug-Like Soft rule.
 Computational Biology & Drug Design Group

MaxRing
Number of atoms in the biggest ring. Number of atoms involved in the biggest system ring. Optimal:0~18, based
on Drug-Like Soft rule.

nHet
Number of heteroatoms. Number of non-carbon atoms (hydrogens included). Optimal:1~15, based on Drug-
Like Soft rule.

fChar
Formal charge. Optimal:-4 ~4, based on Drug-Like Soft rule

nRig
Number of rigid bonds. Number of non-flexible bonds, in opposite to rotatable bonds. Optimal:0~30, based on
Drug-Like Soft rule.

Flexibility
Flexibility = nRot / nRig

Stereo Centers
Number of stereocenters. Optimal: ≤ 2, based on Lead-Like Soft rule.

TPSA
Topological polar surface area. Sum of tabulated surface contributions of polar fragments. Optimal:0~140,
based on Veber rule.

logS
⚫ The logarithm of aqueous solubility value. The first step in the drug absorption process is the disintegration
of the tablet or capsule, followed by the dissolution of the active drug. Low solubility is detrimental to good
and complete oral absorption, and early measurement of this property is of great importance in drug
discovery.
⚫ Results interpretation: The predicted solubility of a compound is given as the logarithm of the molar
concentration (log mol/L). Compounds in the range from -4 to 0.5 log mol/L will be considered proper.

logP
⚫ The logarithm of the n-octanol/water distribution coefficient. log P possess a leading position with
considerable impact on both membrane permeability and hydrophobic binding to macromolecules,
including the target receptor as well as other proteins like plasma proteins, transporters, or metabolizing
enzymes.

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⚫ Results interpretation: The predicted logP of a compound is given as the logarithm of the molar
concentration (log mol/L). Compounds in the range from 0 to 3 log mol/L will be considered proper.

logD7.4
⚫ The logarithm of the n-octanol/water distribution coefficients at pH=7.4. To exert a therapeutic effect, one
drug must enter the blood circulation and then reach the site of action. Thus, an eligible drug usually needs
to keep a balance between lipophilicity and hydrophilicity to dissolve in the body fluid and penetrate the
biomembrane effectively. Therefore, it is important to estimate the n-octanol/water distribution coefficients
at physiological pH (logD7.4) values for candidate compounds in the early stage of drug discovery.
⚫ Results interpretation: The predicted logD7.4 of a compound is given as the logarithm of the molar
concentration (log mol/L). Compounds in the range from 1 to 3 log mol/L will be considered proper.

2. Medicinal Chemistry

QED [1]
⚫ A measure of drug-likeness based on the concept of desirability. QED is calculated by integrating the
outputs of the desirability functions based on eight drug-likeness related properties, including MW, log P,
NHBA, NHBD, PSA, Nrotb, the number of aromatic rings (NAr), and the number of alerts for undesirable
functional groups. Here, average descriptor weights were used in the calculation of QED. The QED score
is calculated by taking the geometric mean of the individual desirability functions, given by 𝑄𝐸𝐷 =
1
𝑒𝑥𝑝 ( ∑𝑛𝑖=1 ln 𝑑𝑖 ), where di indicates the dth desirability function and n = 8 is the number of drug-likeness
𝑛

related properties.
⚫ Results interpretation: The mean QED is 0.67 for the attractive compounds, 0.49 for the unattractive
compounds and 0.34 for the unattractive compounds considered too complex.
⚫ Empirical decision: > 0.67: excellent (green); ≤ 0.67: poor (red)

SAscore [2]
⚫ Synthetic accessibility score is designed to estimate ease of synthesis of drug-like molecules, based on a
combination of fragment contributions and a complexity penalty. The score is between 1 (easy to make)
and 10 (very difficult to make). The synthetic accessibility score (SAscore) is calculated as a combination
of two components: 𝑆𝐴𝑠𝑐𝑜𝑟𝑒 = 𝑓𝑟𝑎𝑔𝑚𝑒𝑛𝑡𝑆𝑐𝑜𝑟𝑒 − 𝑐𝑜𝑚𝑝𝑙𝑒𝑥𝑖𝑡𝑦𝑃𝑒𝑛𝑎𝑙𝑡𝑦.
⚫ Results interpretation: high SAscore: ≥ 6, difficult to synthesize; low SAscore: < 6, easy to synthesize

⚫ Empirical decision: ≤ 6：excellent (green); > 6: poor (red)

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Fsp3 [3]
⚫ Fsp3, the number of sp3 hybridized carbons/total carbon count, is used to determine the carbon saturation
of molecules and characterize the complexity of the spatial structure of molecules. It has been
demonstrated that the increased saturation measured by Fsp3 and the number of chiral centers in the
molecule increase the clinical success rate, which might be related to the increased solubility, or the fact
that the enhanced 3D features allow small molecules to occupy more target space.
⚫ Results interpretation: Fsp3 ≥ 0.42 is considered a suitable value.

⚫ Empirical decision: ≥ 0.42：excellent (green); ＜0.42: poor (red)

MCE-18 [4]
⚫ MCE-18 stands for medicinal chemistry evolution in 2018, and this measure can effectively score molecules
by novelty in terms of their cumulative sp3 complexity. It can effectively score structures by their novelty
and current lead potential in contrast to simple and in many cases false positive sp3 index, and given by
𝑠𝑝3 +𝐶𝑦𝑐−𝐴𝑐𝑦𝑐
the following equation: 𝑀𝐶𝐸18 = (𝐴𝑅 + 𝑁𝐴𝑅 + 𝐶𝐻𝐼𝑅𝐴𝐿 + 𝑆𝑃𝐼𝑅𝑂 + ) × 𝑄1 , where
1+𝑠𝑝3

AR is the presence of an aromatic or heteroaromatic ring (0 or 1), NAR is the presence of an aliphatic or a
heteroaliphatic ring (0 or 1), CHIRAL is the presence of a chiral center (0 or 1), SPIRO is the presence of
a spiro point (0 or 1), sp3 is the portion of sp3-hybridized carbon atoms (from 0 to 1), Cyc is the portion of
cyclic carbons that are sp3 hybridized (from 0 to 1), Acyc is a portion of acyclic carbon atoms that are sp3
hybridized (from 0 to 1), and Q1 is the normalized quadratic index.
⚫ Results interpretation: < 45: uninteresting, trivial, old scaffolds, low degree of 3D complexity and novelty;
45~63: sufficient novelty, basically follow the trends of currently observed in medicinal chemistry; 63~78:
high structural similarity to the compounds disclosed in patent records; >78: need to be inspected visually
to assess their target profile and drug-likeness.

⚫ Empirical decision: ≥ 45：excellent (green); ＜45: poor (red)

NPscore [5]
⚫ The Natural Product-likeness score is a useful measure which can help to guide the design of new
molecules toward interesting regions of chemical space which have been identified as “bioactive regions”
by natural evolution. The calculation consists of molecule fragmentation, table lookup, and summation of
fragment contributions.
⚫ Results interpretation: The calculated score is typically in the range from −5 to 5. The higher the score is,
the higher the probability is that the molecule is a NP.

Lipinski Rule [6]

⚫ Content: MW≤500; logP≤5; Hacc≤10; Hdon≤5
⚫ Results interpretation: If two properties are out of range, a poor absorption or permeability is possible, one
is acceptable.

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⚫ Empirical decision: < 2 violations：excellent (green)；≥2 violations: poor (red)

Pfizer Rule [7]

⚫ Content: logP > 3; TPSA < 75
⚫ Results interpretation: Compounds with a high log P (>3) and low TPSA (<75) are likely to be toxic.
⚫ Empirical decision: two conditions satisfied: poor (red); otherwise: excellent (green)

GSK Rule [8]

⚫ Content: MW ≤ 400; logP ≤ 4
⚫ Results interpretation: Compounds satisfying the GSK rule may have a more favorable ADMET profile.

⚫ Empirical decision: 0 violations: excellent (green); otherwise: poor (red)

Golden Triangle [9]

⚫ Content: 200 ≤MW ≤50; -2 ≤ logD ≤5
⚫ Results interpretation: Compounds satisfying the GoldenTriangle rule may have a more favourable ADMET
profile.
⚫ Empirical decision: 0 violations: excellent (green); otherwise: poor (red)

PAINS [10]
⚫ Pan Assay Interference Compounds (PAINS) is one of the most famous frequent hitters filters, which
comprises 480 substructures derived from the analysis of FHs determined by six target-based HTS assay.
By application of these filters, it is easier to screen false positive hits and to flag suspicious compounds in
screening databases. One of the most authoritative medicine magazines Journal of Medicinal Chemistry
even requires authors to provide the screening results with the PAINS alerts of active compounds when
submitting manuscripts.
⚫ Results interpretation: If the number of alerts is not zero, the users could check the substructures by the
DETIAL button.

ALARM NMR Rule [11]

⚫ Thiol reactive compounds. There are 75 substructures in this endpoint.
⚫ Results interpretation: If the number of alerts is not zero, the users could check the substructures by the
DETIAL button.

BMS Rule [12]

⚫ Undesirable, reactive compounds.There are 176 substructures in this endpoint.
⚫ Results interpretation: If the number of alerts is not zero, the users could check the substructures by the
DETIAL button.

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Chelator Rule [13]

⚫ Chelating compounds. There are 55 substructures in this endpoint.
⚫ Results interpretation: If the number of alerts is not zero, the users could check the substructures by the
DETIAL button.

3. Absorption

Caco-2 Permeability
⚫ Before an oral drug reaches the systemic circulation, it must pass through intestinal cell membranes via
passive diffusion, carrier-mediated uptake or active transport processes. The human colon
adenocarcinoma cell lines (Caco-2), as an alternative approach for the human intestinal epithelium, has
been commonly used to estimate in vivo drug permeability due to their morphological and functional
similarities. Thus, Caco-2 cell permeability has also been an important index for an eligible candidate drug
compound.
⚫ Results interpretation: The predicted Caco-2 permeability of a given compound is given as the log cm/s. A
compound is considered to have a proper Cao-2 permeability if it has predicted value >-5.15log cm/s.
⚫ Empirical decision: > -5.15: excellent (green); otherwise: poor (red)

MDCK Permeability
⚫ Madin−Darby Canine Kidney cells (MDCK) have been developed as an in vitro model for permeability
screening. Its apparent permeability coefficient, Papp, is widely considered to be the in vitro gold standard
for assessing the uptake efficiency of chemicals into the body. Papp values of MDCK cell lines are also used
to estimate the effect of the blood-brain barrier (BBB).
⚫ Results interpretation: The unit of predicted MDCK permeability is cm/s. A compound is considered to have
a high passive MDCK permeability for a Papp > 20 x 10-6 cm/s, medium permeability for 2-20 x 10-6cm/s, low
permeability for < 2 x 10-6cm/s.
⚫ Empirical decision: >2 x 10-6cm/s: excellent (green), otherwise: poor (red)

Pgp-inhibitor
⚫ The inhibitor of P-glycoprotein. The P-glycoprotein, also known as MDR1 or 2 ABCB1, is a membrane
protein member of the ATP-binding cassette (ABC) transporters superfamily. It is probably the most
promiscuous efflux transporter, since it recognizes a number of structurally different and apparently
unrelated xenobiotics; notably, many of them are also CYP3A4 substrates.
⚫ Results interpretation: Category 0: Non-inhibitor; Category 1: Inhibitor. The output value is the probability
of being Pgp-inhibitor, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

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Pgp-substrate
⚫ As described in the Pgp-inhibitor section, modulation of P-glycoprotein mediated transport has significant
pharmacokinetic implications for Pgp substrates, which may either be exploited for specific therapeutic
advantages or result in contraindications.
⚫ Results interpretation: Category 0: Non-substrate; Category 1: substrate. The output value is the probability
of being Pgp-substrate, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

HIA
⚫ Human intestinal absorption. As described above, the human intestinal absorption of an oral drug is the
essential prerequisite for its apparent efficacy. What’s more, the close relationship between oral
bioavailability and intestinal absorption has also been proven and HIA can be seen an alternative indicator
for oral bioavailability to some extent.
⚫ Result interpretation: A molecule with an absorbance of less than 30% is considered to be poorly absorbed.
Accordingly, molecules with a HIA >30% were classified as HIA- (Category 0), while molecules with a HIA
< 30% were classified as HIA+(Category 1). The output value is the probability of being HIA+, within the
range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

F20%
⚫ The human oral bioavailability 20%. For any drug administrated by the oral route, oral bioavailability is
undoubtedly one of the most important pharmacokinetic parameters because it is the indicator of the
efficiency of the drug delivery to the systemic circulation.
⚫ Result interpretation: Molecules with a bioavailability ≥ 20% were classified as F20%- (Category 0), while
molecules with a bioavailability < 20% were classified as F20%+ (Category 1). The output value is the
probability of being F20%+, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

F30%
⚫ The human oral bioavailability 30%. For any drug administrated by the oral route, oral bioavailability is
undoubtedly one of the most important pharmacokinetic parameters because it is the indicator of the
efficiency of the drug delivery to the systemic circulation.
⚫ Result interpretation: Molecules with a bioavailability ≥ 30% were classified as F 30%- (Category 0), while
molecules with a bioavailability < 30% were classified as F30%+ (Category 1). The output value is the
probability of being F30%+, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

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4. Distribution

PPB
⚫ Plasma protein binding. One of the major mechanisms of drug uptake and distribution is through PPB, thus
the binding of a drug to proteins in plasma has a strong influence on its pharmacodynamic behavior. PPB
can directly influence the oral bioavailability because the free concentration of the drug is at stake when a
drug binds to serum proteins in this process.
⚫ Result interpretation: A compound is considered to have a proper PPB if it has predicted value < 90%, and
drugs with high protein-bound may have a low therapeutic index.
⚫ Empirical decision: ≤ 90%: excellent (green); otherwise: poor (red).

VD
⚫ Volume Distribution. The VD is a theoretical concept that connects the administered dose with the actual
initial concentration present in the circulation and it is an important parameter to describe the in vivo
distribution for drugs. In practical, we can speculate the distribution characters for an unknown compound
according to its VD value, such as its condition binding to plasma protein, its distribution amount in body
fluid and its uptake amount in tissues.
⚫ Result interpretation: The unit of predicted VD is L/kg. A compound is considered to have a proper VD if it
has predicted VD in the range of 0.04-20L/kg.
⚫ Empirical decision: 0.04-20: excellent (green); otherwise: poor (red)

BBB Penetration
⚫ Drugs that act in the CNS need to cross the blood–brain barrier (BBB) to reach their molecular target. By
contrast, for drugs with a peripheral target, little or no BBB penetration might be required in order to avoid
CNS side effects.
⚫ Result interpretation: The unit of BBB penetration is cm/s. Molecules with logBB > -1 were classified as
BBB+ (Category 1), while molecules with logBB ≤ -1 were classified as BBB- (Category 0). The output
value is the probability of being BBB+, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

Fu
⚫ The fraction unbound in plasms. Most drugs in plasma will exist in equilibrium between either an unbound
state or bound to serum proteins. Efficacy of a given drug may be affect by the degree to which it binds
proteins within blood, as the more that is bound the less efficiently it can traverse cellular membranes or
diffuse.
⚫ Result interpretation: >20%: High Fu; 5-20%: medium Fu; <5% low Fu.
⚫ Empirical decision: ≥ 5%: excellent (green);< 5%: poor (red).

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5. Metabolism

CYP 1A2 / 2C19 / 2C9 / 2D6 / 3A4 inhibitor

CYP 1A2 / 2C19 /2C9 / 2D6 / 3A4 substrate
⚫ Based on the chemical nature of biotransformation, the process of drug metabolism reactions can be
divided into two broad categories: phase I (oxidative reactions) and phase II (conjugative reactions). The
human cytochrome P450 family (phase I enzymes) contains 57 isozymes and these isozymes metabolize
approximately two-thirds of known drugs in human with 80% of this attribute to five isozymes––1A2, 3A4,
2C9, 2C19 and 2D6. Most of these CYPs responsible for phase I reactions are concentrated in the liver.
⚫ Result interpretation: Category 0: Non-substrate / Non-inhibitor; Category 1: substrate / inhibitor. The output
value is the probability of being substrate / inhibitor, within the range of 0 to 1.

6. Excretion

CL
⚫ The clearance of a drug. Clearance is an important pharmacokinetic parameter that defines, together
with the volume of distribution, the half-life, and thus the frequency of dosing of a drug.
⚫ Result interpretation: The unit of predicted CL penetration is ml/min/kg. >15 ml/min/kg: high clearance; 5-
15 ml/min/kg: moderate clearance; <5 ml/min/kg: low clearance.
⚫ Empirical decision: ≥ 5: excellent (green);< 5: poor (red).

T1/2
⚫ The half-life of a drug is a hybrid concept that involves clearance and volume of distribution, and it is
arguably more appropriate to have reliable estimates of these two properties instead.
⚫ Result interpretation: Molecules with T1/2 > 3 were classified as T1/2 - (Category 0), while molecules with
T1/2 ≤ 3 were classified as T1/2 + (Category 1). The output value is the probability of being T1/2+, within the
range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

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7. Toxicology

hERG Blockers
⚫ The human ether-a-go-go related gene. The During cardiac depolarization and repolarization, a voltage-
gated potassium channel encoded by hERG plays a major role in the regulation of the exchange of cardiac
action potential and resting potential. The hERG blockade may cause long QT syndrome (LQTS),
arrhythmia, and Torsade de Pointes (TdP), which lead to palpitations, fainting, or even sudden death.
⚫ Result interpretation: Molecules with IC50 more than 10 μM or less than 50% inhibition at 10 μM were
classified as hERG - (Category 0), while molecules with IC50 less than 10 μM or more than 50% inhibition
at 10 μM were classified as hERG+ (Category 1). The output value is the probability of being hERG+, within
the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

H-HT
⚫ The human hepatotoxicity. Drug induced liver injury is of great concern for patient safety and a major cause
for drug withdrawal from the market. Adverse hepatic effects in clinical trials often lead to a late and costly
termination of drug development programs.
⚫ Result interpretation: Category 0: H-HT negative(-); Category 1: H-HT positive(+). The output value is the
probability of being toxic, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

DILI
⚫ Drug-induced liver injury (DILI) has become the most common safety problem of drug withdrawal from the
market over the past 50 years.
⚫ Result interpretation: Category 0: DILI negative(-); Category 1: DILI positive(+). The output value is the
probability of being toxic, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

AMES Toxicity
⚫ The Ames test for mutagenicity. The mutagenic effect has a close relationship with the carcinogenicity, and
it is the most widely used assay for testing the mutagenicity of compounds.
⚫ Result interpretation: Category 0: AMES negative(-); Category 1: AMES positive(+). The output value is the
probability of being toxic, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

Rat Oral Acute Toxicity

⚫ Determination of acute toxicity in mammals (e.g. rats or mice) is one of the most important tasks for the
safety evaluation of drug candidates.

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⚫ Result interpretation: Category 0: low-toxicity, > 500 mg/kg; Category 1: high-toxicity; < 500 mg/kg. The
output value is the probability of being toxic, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

FDAMDD
⚫ The maximum recommended daily dose provides an estimate of the toxic dose threshold of chemicals in
humans.
⚫ Result interpretation: Category 1: FDAMDD positive(+), ≤ 0.011 mmol/kg -bw/day; Category 0: FDAMDD
negative(-), > 0.011 mmol/kg-bw/day. The output value is the probability of being toxic, within the range of
0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

Skin Sensitization
⚫ Skin sensitization is a potential adverse effect for dermally applied products. The evaluation of whether a
compound, that may encounter the skin, can induce allergic contact dermatitis is an important safety
concern.
⚫ Result interpretation: Category 1: Sensitizer; Category 0: Non-sensitizer. The output value is the probability
of being toxic, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

Carcinogencity
⚫ Among various toxicological endpoints of chemical substances, carcinogenicity is of great concern because
of its serious effects on human health. The carcinogenic mechanism of chemicals may be due to their ability
to damage the genome or disrupt cellular metabolic processes. Many approved drugs have been identified
as carcinogens in humans or animals and have been withdrawn from the market.
⚫ Result interpretation: Category 1: carcinogens; Category 0: non-carcinogens. Chemicals are labelled as
active (carcinogens) or inactive (non-carcinogens) according to their TD50 values. The output value is the
probability of being toxic, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

Eye Corrosion / Irritation

⚫ Assessing the eye irritation/corrosion (EI/EC) potential of a chemical is a necessary component of risk
assessment. Cornea and conjunctiva tissues comprise the anterior surface of the eye, and hence cornea
and conjunctiva tissues are directly exposed to the air and easily suffer injury by chemicals. There are
several substances, such as chemicals used in manufacturing, agriculture and warfare, ocular
pharmaceuticals, cosmetic products, and household products, that can cause EI or EC.
⚫ Result interpretation: Category 1: corrosives / irritants chemicals; Category 0: non-corrosives / non-irritants
chemicals. The output value is the probability of being toxic, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)
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Respiratory Toxicity
⚫ Among these safety issues, respiratory toxicity has become the main cause of drug withdrawal. Drug-
induced respiratory toxicity is usually underdiagnosed because it may not have distinct early signs or
symptoms in common medications and can occur with significant morbidity and mortality.Therefore, careful
surveillance and treatment of respiratory toxicity is of great importance.
⚫ Result interpretation: Category 1: respiratory toxicants; Category 0: non-respiratory toxicants. The output
value is the probability of being toxic, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

Bioconcentration Factor
⚫ The bioconcentration factor BCF is defined as the ratio of the chemical concentration in biota as a result of
absorption via the respiratory surface to that in water at steady state. It is used for considering secondary
poisoning potential and assessing risks to human health via the food chain. The unit of BCF is log10(L/kg).

IGC50
⚫ 48 hour Tetrahymena pyriformis IGC50 (concentration of the test chemical in water in mg/L that causes 50%
growth inhibition to Tetrahymena pyriformis after 48 hours). The unit of IGC50 is −log10[(mg/L)/(1000*MW)].

LC50FM
⚫ 96 hour fathead minnow LC50 (concentration of the test chemical in water in mg/L that causes 50% of
fathead minnow to die after 96 hours). The unit of LC50FM is −log10[(mg/L)/(1000*MW)].

LC50DM
⚫ 48 hour Daphnia magna LC50 (concentration of the test chemical in water in mg/L that causes 50% of
Daphnia magna to die after 48 hours). The unit of LC50DM is −log10[(mg/L)/(1000*MW)].

NR-AR
⚫ Androgen receptor (AR), a nuclear hormone receptor, plays a critical role in AR-dependent prostate cancer
and other androgen related diseases. Endocrine disrupting chemicals (EDCs) and their interactions with
steroid hormone receptors like AR may cause disruption of normal endocrine function as well as interfere
with metabolic homeostasis, reproduction, developmental and behavioral functions.
⚫ Result interpretation: Category 1: actives ; Category 0: inactives. The output value is the probability of being
AR agonists, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

NR-AR-LBD
⚫ Androgen receptor (AR), a nuclear hormone receptor, plays a critical role in AR-dependent prostate cancer
and other androgen related diseases. Endocrine disrupting chemicals (EDCs) and their interactions with

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steroid hormone receptors like AR may cause disruption of normal endocrine function as well as interfere
with metabolic homeostasis, reproduction, developmental and behavioral functions.
⚫ Result interpretation: Category 1: actives ; Category 0: inactives. Molecules that labeled 1 in this bioassay
may bind to the LBD of androgen receptor. The output value is the probability of being actives, within the
range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

NR-AhR
⚫ The Aryl hydrocarbon Receptor (AhR), a member of the family of basic helix-loop-helix transcription factors,
is crucial to adaptive responses to environmental changes. AhR mediates cellular responses to
environmental pollutants such as aromatic hydrocarbons through induction of phase I and II enzymes but
also interacts with other nuclear receptor signaling pathways.
⚫ Result interpretation: Category 1: actives ; Category 0: inactives. Molecules that labeled 1 may activate the
aryl hydrocarbon receptor signaling pathway. The output value is the probability of being actives, within the
range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

NR-Aromatase
⚫ Endocrine disrupting chemicals (EDCs) interfere with the biosynthesis and normal functions of steroid
hormones including estrogen and androgen in the body. Aromatase catalyzes the conversion of androgen
to estrogen and plays a key role in maintaining the androgen and estrogen balance in many of the EDC-
sensitive organs.
⚫ Result interpretation: Category 1: actives ; Category 0: inactives. Molecules that labeled 1 are regarded as
aromatase inhibitors that could affect the balance between androgen and estrogen. The output value is the
probability of being actives, within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

NR-ER
⚫ Estrogen receptor (ER), a nuclear hormone receptor, plays an important role in development, metabolic
homeostasis and reproduction. Endocrine disrupting chemicals (EDCs) and their interactions with steroid
hormone receptors like ER causes disruption of normal endocrine function. Therefore, it is important to
understand the effect of environmental chemicals on the ER signaling pathway.
⚫ Result interpretation: Category 1: actives ; Category 0: inactives. The output value is the probability of being
actives within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

NR-ER-LBD
⚫ Estrogen receptor (ER), a nuclear hormone receptor, plays an important role in development, metabolic
homeostasis and reproduction. Two subtypes of ER, ER-alpha and ER-beta have similar expression
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patterns with some uniqueness in both types. Endocrine disrupting chemicals (EDCs) and their interactions
with steroid hormone receptors like ER causes disruption of normal endocrine function.
⚫ Result interpretation: Category 1: actives ; Category 0: inactives. The output value is the probability of being
actives within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

NR-PPAR-gamma
⚫ The peroxisome proliferator-activated receptors (PPARs) are lipid-activated transcription factors of the
nuclear receptor superfamily with three distinct subtypes namely PPAR alpha, PPAR delta (also called
PPAR beta) and PPAR gamma (PPARg). All these subtypes heterodimerize with Retinoid X receptor (RXR)
and these heterodimers regulate transcription of various genes. PPAR-gamma receptor (glitazone receptor)
is involved in the regulation of glucose and lipid metabolism.
⚫ Result interpretation: Category 1: actives ; Category 0: inactives. The output value is the probability of being
actives within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

SR-ARE
⚫ Oxidative stress has been implicated in the pathogenesis of a variety of diseases ranging from cancer to
neurodegeneration. The antioxidant response element (ARE) signaling pathway plays an important role in
the amelioration of oxidative stress. The CellSensor ARE-bla HepG2 cell line (Invitrogen) can be used for
analyzing the Nrf2/antioxidant response signaling pathway. Nrf2 (NF-E2-related factor 2) and Nrf1 are
transcription factors that bind to AREs and activate these genes.
⚫ Result interpretation: Category 1: actives ; Category 0: inactives. The output value is the probability of being
actives within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

SR-ATAD5
⚫ ATPase family AAA domain-containing protein 5. As cancer cells divide rapidly and during every cell
division they need to duplicate their genome by DNA replication. The failure to do so results in the cancer
cell death. Based on this concept, many chemotherapeutic agents were developed but have limitations
such as low efficacy and severe side effects etc. Enhanced Level of Genome Instability Gene 1 (ELG1;
human ATAD5) protein levels increase in response to various types of DNA damage.
⚫ Result interpretation: Category 1: actives ; Category 0: inactives. The output value is the probability of being
actives within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

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SR-HSE
⚫ Heat shock factor response element. Various chemicals, environmental and physiological stress
conditions may lead to the activation of heat shock response/ unfolded protein response (HSR/UPR). There
are three heat shock transcription factors (HSFs) (HSF-1, -2, and -4) mediating transcriptional regulation
of the human HSR.
⚫ Result interpretation: Category 1: actives ; Category 0: inactives. The output value is the probability of being
actives within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

SR-MMP
⚫ Mitochondrial membrane potential (MMP), one of the parameters for mitochondrial function, is generated
by mitochondrial electron transport chain that creates an electrochemical gradient by a series of redox
reactions. This gradient drives the synthesis of ATP, a crucial molecule for various cellular processes.
Measuring MMP in living cells is commonly used to assess the effect of chemicals on mitochondrial function;
decreases in MMP can be detected using lipophilic cationic fluorescent dyes.
⚫ Result interpretation: Category 1: actives ; Category 0: inactives. The output value is the probability of being
actives within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

SR-p53
⚫ p53, a tumor suppressor protein, is activated following cellular insult, including DNA damage and other
cellular stresses. The activation of p53 regulates cell fate by inducing DNA repair, cell cycle arrest,
apoptosis, or cellular senescence. The activation of p53, therefore, is a good indicator of DNA damage and
other cellular stresses.
⚫ Result interpretation: Category 1: actives ; Category 0: inactives. The output value is the probability of being
actives within the range of 0 to 1.
⚫ Empirical decision: 0-0.3: excellent (green); 0.3-0.7: medium (yellow); 0.7-1.0(++): poor (red)

Acute Toxicity Rule

⚫ Molecules containing these substructures may cause acute toxicity during oral administration. There are
20 substructures in this endpoint.
⚫ Results interpretation: If the number of alerts is not zero, the users could check the substructures by the
DETIAL button.

Genotoxic Carcinogenicity Rule

⚫ Molecules containing these substructures may cause carcinogenicity or mutagenicity through genotoxic
mechanisms.There are 117 substructures in this endpoint.

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⚫ Results interpretation: If the number of alerts is not zero, the users could check the substructures by the
DETIAL button.

NonGenotoxic Carcinogenicity Rule

⚫ Molecules containing these substructures may cause carcinogenicity through nongenotoxic mechanisms.
There are 23 substructures in this endpoint.
⚫ Results interpretation: If the number of alerts is not zero, the users could check the substructures by the
DETIAL button.

Skin Sensitization Rule

⚫ Molecules containing these substructures may cause skin irritation.There are 155 substructures in this
endpoint. Molecules containing these substructures may cause skin irritation.
⚫ Results interpretation: If the number of alerts is not zero, the users could check the substructures by the
DETIAL button.

Aquatic Toxicity Rule

⚫ Molecules containing these substructures may cause toxicity to liquid(water). There are 99 substructures
in this endpoint.
⚫ Results interpretation: If the number of alerts is not zero, the users could check the substructures by the
DETIAL button.

NonBiodegradable Rule
⚫ Molecules containing these substructures may be non-biodegradable. There are 19 substructures in this
endpoint.
⚫ Results interpretation: If the number of alerts is not zero, the users could check the substructures by the
DETIAL button.

SureChEMBL Rule
⚫ Molecules matching one or more structural alerts are considered to have MedChem unfriendly status.
There are 164 substructures in this endpoint.
⚫ Results interpretation: If the number of alerts is not zero, the users could check the substructures by the
DETIAL button.

FAF-Drugs4 Rule
⚫ Molecules containing these substructures may be toxic. There are 154 substructures collected form FAF-
Drugs4 webserver in this endpoint.
Results interpretation: If the number of alerts is not zero, the users could check the substructures by the
DETIAL button.

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