A Novel Logic-Based Approach for Quantitative Toxicology Prediction

Abstract

There is a pressing need for accurate in silico methods to predict the toxicity of molecules that are being introduced into the environment or are being developed into new pharmaceuticals. Predictive toxicology is in the realm of structure activity relationships (SAR), and many approaches have been used to derive such SAR. Previous work has shown that inductive logic programming (ILP) is a powerful approach that circumvents several major difficulties, such as molecular superposition, faced by some other SAR methods. The ILP approach reasons with chemical substructures within a relational framework and yields chemically understandable rules. Here, we report a general new approach, support vector inductive logic programming (SVILP), which extends the essentially qualitatiVe ILP-based SAR to quantitatiVe modeling. First, ILP is used to learn rules, the predictions of which are then used within a novel kernel to derive a support-vector generalization model. For a highly heterogeneous dataset of 576 molecules with known fathead minnow fish toxicity, the cross-validated correlation coefficients (R 2 CV ) from a chemical descriptor method (CHEM) and SVILP are 0.52 and 0.66, respectively. The ILP, CHEM, and SVILP approaches correctly predict 55, 58, and 73%, respectively, of toxic molecules. In a set of 165 unseen molecules, the R 2 values from the commercial software TOPKAT and SVILP are 0.26 and 0.57, respectively. In all calculations, SVILP showed significant improvements in comparison with the other methods. The SVILP approach has a major advantage in that it uses ILP automatically and consistently to derive rules, mostly novel, describing fragments that are toxicity alerts. The SVILP is a general machine-learning approach and has the potential of tackling many problems relevant to chemoinformatics including in silico drug design.

Review of (Quantitative) Structure–Activity Relationships for Acute Aquatic Toxicity

“…In the clustering first, then modelling approach, the authors concluded that local models perform better in predicting acute fish toxicity than global ones. A support vector inductive logic programming was applied to the same endpoint and a diverse set of chemicals [6]. The approach first learns rules from data, followed by quantitative modelling.…”

Multi‐class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods

“…Support Vector Inductive Logic Programming (SVILP) [8,11] is a new machine learning technique that combines Inductive Logic Programming and Support Vector Machines. It can be viewed as a multistage learning algorithm.…”

“…[8,11] It solves binary classification and regression problems in a multistage learning process. In the first stage, a set of first order rules is obtained from an ILP system that takes relationally encoded compounds and background knowledge (like atom bond descriptions, functional groups, etc.)…”

Computational Intelligence in Medicine and Biology: A Survey

“…Since nowadays we speak about molecular and personalized medicine, 179 drug design should also be examined from this point of view. Therefore, there have been developed methodologies that include molecular modelling, molecular dynamics and docking procedures [180][181][182][183][184] in order to simulate the feasibility of interactions and the related affinity between ligand and target in order to acquire strong in silico evidences for drug design [185][186][187][188][189][190][191][192][193][194][195] and finally drug production.…”