Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs
Fig 4
(A) Number of hits retrieved by using tanimoto score with featmorgan as similarity measure, which grows markedly as threshold is reduced (lower threshold means less similarity). (B) correlation between RF models’ average probability predictions >0.5 with corresponding tanimoto score of featmorgan of drug-food pairs. Our hit pair triflusal and 5-methoxysalicylic acid (highlighted in red) was predicted a hit by RF models (as top 219th pair) would be missed by featmorgan if used alone. (C) Rank comparison between hit pair (Triflusal:5-methoxysalicylic acid) and the negative control (Triflusal:4-isopropylbenzoic acid). The negative control was not a hit using RF models although had a higher rank with featmorgan than the hit pair and vice versa.