Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Small molecule developmental screens reveal the logic and timing of vertebrate development

Much has been learned about vertebrate development by random mutagenesis followed by phenotypic screening and by targeted gene disruption followed by phenotypic analysis in model organisms. Because the timing of many developmental events is critical, it would be useful to have temporal control over modulation of gene function, a luxury frequently not possible with genetic mutants. Here, we demonstrate that small molecules capable of conditional gene product modulation can be identified through developmental screens in zebrafish. We have identified several small molecules that specifically modulate various aspects of vertebrate ontogeny, including development of the central nervous system, the cardiovascular system, the neural crest, and the ear. Several of the small molecules identified allowed us to dissect the logic of melanocyte and otolith development and to identify critical periods for these events. Small molecules identified in this way offer potential to dissect further these and other developmental processes and to identify novel genes involved in vertebrate development.

Structure and function of declarative and nondeclarative memory systems

This article reviews recent studies of memory systems in humans and nonhuman primates. Three major conclusions from recent work are that (i) the capacity for nondeclarative (nonconscious) learning can now be studied in a broad array of tasks that assess classification learning, perceptuomotor skill learning, artificial grammar learning, and prototype abstraction; (ii) cortical areas adjacent to the hippocampal formation, including entorhinal, perirhinal, and parahippocampal cortices, are an essential part of the medial temporal lobe memory system that supports declarative (conscious) memory; and (iii) in humans, bilateral damage limited to the hippocampal formation is nevertheless sufficient to produce severe anterograde amnesia and temporally graded retrograde amnesia covering as much as 25 years.