Yes

YesDisplay

prediction Find

hypotheses

For each hypothesis find kNN over example set k=10

Check if this is an exact

match

Querycompound

No

Fragment structure

Yes

Display prediction

Fragment structure

Find hypotheses

Are all fragments adequately represented

in the training set?

No

No

Yes

Find kNN over whole training

set k=8

Calculate confidence level

confidence x = | Sx |

Display prediction

Calculate confidence level for eachindividual hypothesis based on the kNN

confidence h,x = | Sh,x |

Out ofdomain

Out ofdomain

Are all fragments adequately represented in the training set?

Calculate overall confidence level confidence x = | Sx |

Apply reasoning

No

Assign 100%confidence

Sx overall signal for the query compound x

signal strength of each individual hypothesis given the query xSh,x

How Sarah Nexus Predicts

* Process correct for Sarah Nexus v2.0.1

Yes

YesDisplay

prediction Find

hypotheses

For each hypothesis find kNN over example set k=10

Check if this is an exact

match

Querycompound

No

Fragment structure

Yes

Display prediction

Fragment structure

Find hypotheses

Are all fragments adequately represented

in the training set?

No

No

Yes

Find kNN over whole training

set k=8

1. Calculate the weighted signal

Wi,x = similarity (x, ei)

2. Further moderate the signal to accountfor the average distance of the

examples from the query structure

3. Calculate confidence levelconfidence x = | Sx |

Display prediction

1. Calculate the weighted signal for each hypothesis based on the kNN

Si = -1 if ei is a negative exampleSi = +1 if ei is a positive example

2. Further moderate the signal to accountfor the average distance of the

examples from the query structure

3. Calculate confidence level for each individual hypothesis

confidence h,x = | Sh,x |

Out ofdomain

Out ofdomain

Are all fragments adequately represented in the training set?

Apply reasoning

No

Assign 100%confidence

WSx = ∑k Wi,x × Si i=1

∑k Wi,x i=1

Sx = WSx x∑k Wi,xi=1

k

Wi,x = similarity (x, ei)

WSh,x = ∑k Wi,x × Si i=1

∑k Wi,x i=1

Sh,x = WSh,x x ∑k Wi,xi=1

k

Calculate overall confidence level confidence x = | Sx |

Sx = ∑m Sh,x × confidenceh,xh=1

signal of the nearest ith neighbour

WSh,x weighted signal of each individual hypothesis given the query x

weighting factor Wi,x

signal strength of each individual hypothesis given the query xSh,x

Si

overall signal for the query compound x Sx

number of relevant hypotheses m

k number of nearest neighbours examples in the training setei

weighted signal from nearest neighbours given the query xWSx

How Sarah Nexus Predicts – Detailed

* Process correct for Sarah Nexus v2.0.1

∑m confidenceh,xh=1