SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules.

We have made available a database of over 1 billion compounds predicted to be easily synthesizable, called Synthetically Accessible Virtual Inventory (SAVI). They have been created by a set of transforms based on an adaptation and extension of the CHMTRN/PATRAN programming languages describing chemical synthesis expert knowledge, which originally stem from the LHASA project. The chemoinformatics toolkit CACTVS was used to apply a total of 53 transforms to about 150,000 readily available building blocks (enamine.net). Only single-step, two-reactant syntheses were calculated for this database even though the technology can execute multi-step reactions. The possibility to incorporate scoring systems in CHMTRN allowed us to subdivide the database of 1.75 billion compounds in sets according to their predicted synthesizability, with the most-synthesizable class comprising 1.09 billion synthetic products. Properties calculated for all SAVI products show that the database should be well-suited for drug discovery. It is being made publicly available for free download from https://doi.org/10.35115/37n9-5738.

Adapting CHMTRN (CHeMistry TRaNslator) for a New Use.

We have adopted and extended the CHMTRN language and used it for the knowledge base of a computer program to generate a large database of synthetically accessible, drug-like chemical structures, the Synthetically Accessible Virtual Inventory (SAVI) Database. CHMTRN is a powerful language originally developed in the LHASA (Logic and Heuristics Applied to Synthetic Analysis) project at Harvard University and used together with the chemical pattern description language, PATRAN, to describe chemical retro-reactions. The languages have proven to be useful beyond the design of retrosynthetic routes and have the potential for much wider use in chemistry; this paper describes CHMTRN and PATRAN as now reimplemented for the forward-synthetic SAVI project but able to describe both forward and retro-reactions.

Establishing Good Computer Modelling Practice (GCMP) in the Prediction of Chemical Toxicity.

This paper suggests guidelines for good computer modelling practice (GCMP) when predicting chemical toxicity, with similar purposes to those for Good Laboratory Practice (GLP). The purpose of GCMP is not to specify what should be delivered with models or predictions but to set out what must be done to ensure that work can be audited, on site, in a way analogous to the auditing of studies conforming to GLP; it is intended to confirm that work has been done properly, as distinct from providing advice on how to do it. Comments are made on the guidelines and how they might be followed, based on practical experience with the implementation of such a scheme in the development of knowledge-based and quantitative structure activity relationship models. It is hoped that publication of this paper will encourage wider discussion of the subject leading to adoption of measures to ensure the trustworthiness of computer modelling work that is carried out in connection with regulatory submissions.

Assessing Confidence in Predictions Using Veracity and Utility - A Case Study on the Prediction of Mammalian Metabolism by Meteor Nexus.

A previous paper1 described new metrics, veracity and utility, for assessing the performance of toxicity prediction systems that report confidence in their predictions. Assessing the performance of systems that predict mammalian metabolism is complicated by the absence of comprehensive sets of negative observations and predictions. This paper presents an approach to assessing the performance of such systems using veracity and utility.

Emerging pattern mining to aid toxicological knowledge discovery.

Knowledge-based systems for toxicity prediction are typically based on rules, known as structural alerts, that describe relationships between structural features and different toxic effects. The identification of structural features associated with toxicological activity can be a time-consuming process and often requires significant input from domain experts. Here, we describe an emerging pattern mining method for the automated identification of activating structural features in toxicity data sets that is designed to help expedite the process of alert development. We apply the contrast pattern tree mining algorithm to generate a set of emerging patterns of structural fragment descriptors. Using the emerging patterns it is possible to form hierarchical clusters of compounds that are defined by the presence of common structural features and represent distinct chemical classes. The method has been tested on a large public in vitro mutagenicity data set and a public hERG channel inhibition data set and is shown to be effective at identifying common toxic features and recognizable classes of toxicants. We also describe how knowledge developers can use emerging patterns to improve the specificity and sensitivity of an existing expert system.

A bioactivity versus ethnobotanical survey of medicinal plants from Nigeria, west Africa.

Traditional medicinal practices play a key role in health care systems in countries with developing economies. The aim of this survey was to validate the use of traditional medicine within local Nigerian communities. In this review, we examine the ethnobotanical uses of selected plant species from the Nigerian flora and attempt to correlate the activities of the isolated bioactive principles with known uses of the plant species in African traditional medicine. Thirty-three (33) plant species were identified and about 100 out of the 120 compounds identified with these plants matched with the ethnobotanical uses of the plants.

How "drug-like" are naturally occurring anti-cancer compounds?

We attempt to evaluate the "drug-likeness" of a collection of ∼1500 natural products, exhibiting in vitro or in vivo activities against cancers of various forms, by using a set of calculated molecular descriptors. Compliance to Lipinski's "Rule of Five" and Jorgensen's "Rule of Three" have been used to assess oral availability, by making use of popular parameters like molecular weights, predicted lipophilicities, number of hydrogen bond donors/acceptors, predicted aqueous solubilities, number of primary metabolites and Caco-2 permeabilities. Meanwhile 24 descriptors have been used to predict properties related to the absorption, distribution, metabolism, elimination, and toxicity (ADMET). The ADMET profiles of the anticancer natural products have been analyzed in comparision with the range of properties for 95 % of known drugs. Our results show that the computed parameters fall within the recommended range for about 42 % of the studied compounds, while respectively 63 % and 69 % of the corresponding 'drug-like' and 'lead-like' subsets had properties predicted to fall within the recommended range for 95 % of known drugs. The aim of giving a picture of how drug-like they are and bring out the need to return to natural sources in searching for anticancer lead compounds.

Assessing the pharmacokinetic profile of the CamMedNP natural products database: an in silico approach.

BACKGROUND: Drug metabolism and pharmacokinetic (DMPK) assessment has come to occupy a place of interest during the early stages of drug discovery today. Computer-based methods are slowly gaining ground in this area and are often used as initial tools to eliminate compounds likely to present uninteresting pharmacokinetic profiles and unacceptable levels of toxicity from the list of potential drug candidates, hence cutting down the cost of the discovery of a drug. RESULTS: In the present study, we present an in silico assessment of the DMPK profile of our recently published natural products database of 1,859 unique compounds derived from 224 species of medicinal plants from the Cameroonian forest. In this analysis, we have used 46 computed physico-chemical properties or molecular descriptors to predict the absorption, distribution, metabolism and elimination (ADME) of the compounds. This survey demonstrated that about 50% of the compounds within the Cameroonian medicinal plant and natural products (CamMedNP) database are compliant, having properties which fall within the range of ADME properties of >95% of currently known drugs, while >73% of the compounds have ≤2 violations. Moreover, about 72% of the compounds within the corresponding 'drug-like' subset showed compliance. CONCLUSIONS: In addition to the previously verified levels of 'drug-likeness' and the diversity and the wide range of measured biological activities, the compounds in the CamMedNP database show interesting DMPK profiles and, hence, could represent an important starting point for hit/lead discovery from medicinal plants in Africa.

Cameroonian medicinal plants: a bioactivity versus ethnobotanical survey and chemotaxonomic classification.

BACKGROUND: In Cameroon herbs are traditionally used to meet health care needs and plans are on the way to integrate traditional medicine in the health care system, even though the plans have not been put into action yet. The country however has a rich biodiversity, with ~8,620 plant species, some of which are commonly used in the treatment of several microbial infections and a range of diseases (malaria, trypanosomiasis, leishmaniasis, diabetes and tuberculosis). METHODS: Our survey consisted in collecting published data from the literature sources, mainly from PhD theses in Cameroonian university libraries and also using the author queries in major natural product and medicinal chemistry journals. The collected data includes plant sources, uses of plant material in traditional medicine, plant families, region of collection of plant material, isolated metabolites and type (e.g. flavonoid, terpenoid, etc.), measured biological activities of isolated compounds, and any comments on significance of isolated metabolites on the chemotaxonomic classification of the plant species. This data was compiled on a excel sheet and analysed. RESULTS: In this study, a literature survey led to the collection of data on 2,700 secondary metabolites, which have been previously isolated or derived from Cameroonian medicinal plants. This represents distinct phytochemicals derived from 312 plant species belonging to 67 plant families. The plant species are investigated in terms of chemical composition with respect to the various plant families. A correlation between the known biological activities of isolated compounds and the ethnobotanical uses of the plants is also attempted. Insight into future direction for natural product search within the Cameroonian forest and Savanna is provided. CONCLUSIONS: It can be verified that a phytochemical search of active secondary metabolites, which is inspired by knowledge from the ethnobotanical uses of medicinal plants could be very vital in a drug discovery program from plant-derived bioactive compounds.

In silico drug metabolism and pharmacokinetic profiles of natural products from medicinal plants in the Congo basin.

PURPOSE: Drug metabolism and pharmacokinetics (DMPK) assessment has come to occupy a place of interest during the early stages of drug discovery today. The use of computer modelling to predict the DMPK and toxicity properties of a natural product library derived from medicinal plants from Central Africa (named ConMedNP). Material from some of the plant sources are currently employed in African Traditional Medicine. METHODS: Computer-based methods are slowly gaining ground in this area and are often used as preliminary criteria for the elimination of compounds likely to present uninteresting pharmacokinetic profiles and unacceptable levels of toxicity from the list of potential drug candidates, hence cutting down the cost of discovery of a drug. In the present study, we present an in silico assessment of the DMPK and toxicity profile of a natural product library containing ~3,200 compounds, derived from 379 species of medicinal plants from 10 countries in the Congo Basin forests and savannas, which have been published in the literature. In this analysis, we have used 46 computed physico-chemical properties or molecular descriptors to predict the absorption, distribution, metabolism and elimination and toxicity (ADMET) of the compounds. RESULTS: This survey demonstrated that about 45% of the compounds within the ConMedNP compound library are compliant, having properties which fall within the range of ADME properties of 95% of currently known drugs, while about 69% of the compounds have ≤ 2 violations. Moreover, about 73% of the compounds within the corresponding "drug-like" subset showed compliance. CONCLUSIONS: In addition to the verified levels of "drug-likeness", diversity and the wide range of measured biological activities, the compounds from medicinal plants in Central Africa show interesting DMPK profiles and hence could represent an important starting point for hit/lead discovery.

Automating knowledge discovery for toxicity prediction using jumping emerging pattern mining.

The design of new alerts, that is, collections of structural features observed to result in toxicological activity, can be a slow process and may require significant input from toxicology and chemistry experts. A method has therefore been developed to help automate alert identification by mining descriptions of activating structural features directly from toxicity data sets. The method is based on jumping emerging pattern mining which is applied to a set of toxic and nontoxic compounds that are represented using atom pair descriptors. Using the resulting jumping emerging patterns, it is possible to cluster toxic compounds into groups defined by the presence of shared structural features and to arrange the clusters into hierarchies. The methodology has been tested on a number of data sets for Ames mutagenicity, oestrogenicity, and hERG channel inhibition end points. These tests have shown the method to be effective at clustering the data sets around minimal jumping-emerging structural patterns and finding descriptions of potentially activating structural features. Furthermore, the mined structural features have been shown to be related to some of the known alerts for all three tested end points.

Towards the creation of an international toxicology information centre.

A pilot toxicology database system has been created which is accessible on-line via the world-wide web or in-house via an intranet. It is intended to be suitable as a source of toxicological information and to support structure-activity relationship studies, and it can be searched on chemical structural and substructural as well as toxicological and physico-chemical data. Successful completion of the pilot has led to an ongoing project to develop and expand the system.

A comprehensive approach to argumentation.

A reasoning model, based on the logic of argumentation, is described. The model represents argumentation as a directed graph in which nodes and arcs can be colored using an ordinal set of weightings and in which the attributes of both nodes and arcs can be modified. It is thus able to deal with the undercutting or augmenting of arguments. Weightings can be propagated through the graph to generate unique weightings for any node or arc. The model is able to deal with contradiction. It can incorporate numerical methods and is able to handle qualitative and quantitative reasoning.

Using absolute and relative reasoning in the prediction of the potential metabolism of xenobiotics.

To be useful, a system which predicts the metabolic fate of a chemical should predict the more likely metabolites rather than every possibility. Reasoning can be used to prioritize biotransformations, but a real biochemical domain is complex and cannot be fully defined in terms of the likelihood of events. This paper describes the combined use of two models for reasoning under uncertainty in a working system, METEOR-one model deals with absolute reasoning and the second with relative reasoning.

Using argumentation for absolute reasoning about the potential toxicity of chemicals.

The application of a new argumentation model is illustrated by reference to DEREK for Windows, a knowledge-based expert system for the prediction of the toxicity of chemicals. Examples demonstrate various aspects of the model such as the undercutting of arguments, the resolution of multiple arguments about the same proposition, and the propagation of arguments along a chain of reasoning.