Paul W. Finn

Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL

This paper presents a case study of a machine-aided knowledge discovery process within the general area of drug design. Within drug design, the particular problem of pharmacophore discovery is isolated, and the Inductive Logic Programming (ILP) system progol is applied to the problem of identifying potential pharmacophores for ACE inhibition. The case study reported in this paper supports four general lessons for machine learning and knowledge discovery, as well as more specific lessons for pharmacophore discovery, for Inductive Logic Programming, and for ACE inhibition. The general lessons for machine learning and knowledge discovery are as follows.1. An initial rediscovery step is a useful tool when approaching a new application domain.2. General machine learning heuristics may fail to match the details of an application domain, but it may be possible to successfully apply a heuristic-based algorithm in spite of the mismatch.3. A complete search for all plausible hypotheses can provide useful information to a user, although experimentation may be required to choose between competing hypotheses.4. A declarative knowledge representation facilitates the development and debugging of background knowledge in collaboration with a domain expert, as well as the communication of final results.

Computational approaches to drug design

Abstract. The rational approach to pharmaceutical drug design begins with an investigation of the relationship between chemical structure and biological activity. Information gained from this analysis is used to aid the design of new, or improved, drugs. Primary considerations during this investigation are the geometric and chemical characteristics of the molecules. Computational chemists who are involved in rational drug design routinely use an array of programs to compute, among other things, molecular surfaces and molecular volume, models of receptor sites, dockings of ligands inside protein cavities, and geometric invariants among different molecules that exhibit similar activity. There is a pressing need for efficient and accurate solutions to the above problems. {Often, limiting assumptions need to be made, in order to make the calculations tractable. Also,} the amount of data processed when searching for a potential drug is currently very large and is only expected to grow larger in the future. This paper describes some areas of computer-aided drug design that are important to computational chemists but are also rich in algorithmic problems. It surveys recent work in these areas both from the computational chemistry and the computer science literature.

A randomized kinematics-based approach to pharmacophore-constrained conformational search and database screening

Computational tools have greatly expedited the pharmaceutical drug design process in recent years. One common task in this process is the search of a large library for small molecules that can achieve both a low‐energy conformation and a prescribed pharmacophore. The pharmacophore expresses constraints on the 3D structure of the molecule by specifying relative atom positions that should be maintained to increase the likelihood that the molecule will bind with the receptor site. This article presents a pharmacophore‐based database screening system that has been designed, implemented, and tested on a molecular database. The primary focus of this article is on a simple, randomized conformational search technique that attempts to simultaneously reduce energy and maintain pharmacophore constraints. This enables the identification of molecules in a database that are likely to dock with a given protein, which can serve as a powerful aid in the search for better drug candidates.

RAPID: randomized pharmacophore identification for drug design

This paper describes a randomized approach for finding invariants in a set of flexible ligands (drug molecules) that underlies an integrated software system called RAPID currently under development. An invariant is a collection of features embedded in 3 which is present in one or more of the possible low-energy conformations of each ligand. Such invariants of chemically distinct molecules are useful for computational chemists since they may represent candidate pharmacophores. A pharmacophore contains the parts of the ligand that are primarily responsible for its interaction and binding with a specific receptor. It is regarded as an inverse image of a receptor and is used as a template for building more effective pharmaceutical drugs. The identification of pharmacophores is crucial in drug design since the structure of the targeted receptor is frequently unknown, but a number of molecules that interact with the receptor have been discovered by experiments. It is expected that our techniques and the results produced by our system will prove useful in other applications such as molecular database screening and comparative molecular field analysis.

Geometric Manipulation of Flexible Ligands

In recent years an effort has been made to supplement traditional methods for drug discovery by computer-assisted “structure-based design.” The structure-based approach involves (among other issues) reasoning about the geometry of drug molecules (or ligands) and about the different spatial conformations that these molecules can attain. This is a preliminary report on a set of tools that we are devising to assist the chemist in the drug design process. We describe our work on the following three topics: (i) geometric data structures for representing and manipulating molecules; (ii) conformational analysis—searching for low-energy conformations; and (iii) pharmacophore identification—searching for common features among different ligands that exhibit similar activity.

Asymmetric synthesis of β-amino acid derivatives by Michael addition to chiral 2-aminomethylacrylates

Abstract The addition of lithium enolates to chiral aminomethylacrylates 7 and 8 proceeded with excellent diastereodifferentiation (up to 98% de) and provided an expeditious synthesis of homochiral β-aminomethylglutarates 9 and 10 , on a scale of up to 500g. The acrylates 7 and 8 , and their antipodes, should be useful synthons for the synthesis of β-amino acid derivatives.

Computer-based screening of compound databases for the identification of novel leads

In the increasingly competitive pharmaceutical industry, novel targets and mechanisms of action are being investigated in the search for the high-quality drugs required by patients and regulatory authorities. As a result of this move towards novel targets, the initial stage of drug discovery, finding a lead structure, becomes increasingly difficult. Computer-based screening is gaining recognition as a key tool for this part of the drug discovery process. A symbiosis of computational analysis and high-speed synthesis offers exciting potential for the future.

Fast and accurate predictions of relative binding energies

Abstract We report on the development of a fast, empirical method for estimating the binding affinity of protein-ligand complexes. The method is of comparable accuracy to existing empirical methods, but uses fewer parameters. We also report on experiments, using a combined quantum mechanical/molecular mechanical (QM/MM) approach, applied to a dataset of thermolysin inhibitors, with improved performance.

Efficient database screening for rational drug design using pharmacophore-constrained conformational search

Computational tools have greatly expedited the pharmaceutical drug design process in recent years. One common task in this process is the search of a large library for small molecules that can achieve both a low-energy conformation and a prescribed pharmacophore. The pharmacophore expresses constraints on the 3D structure of the molecule by specifying relative atom positions that should be maintained to increase the likelihood that the molecule will bind with the receptor site. This paper presents a pharmacophorebased database screening system that has been designed, implemented, and tested on a molecular database. The key ingredient in this system is a simple, randomized conformational search technique that attempts to simultaneously reduce energy and maintain pharmacophore constraints. This enables efficient identification of molecules in a database that are likely to dock with a given protein, which can serve as a powerful aid in the search for better drug candidates.

Search techniques for rational drug design

Pharmaceutical drug design is a long and expensive process. Early selection of promising molecules can dramatically improve this process, but this selection is often similar to searching for a needle in a haystack. In most cases, all that a chemist initially has is a small collection of molecules that exhibit enough desired activity to hypothesize that they share a 3D atomic substructure binding to the same receptor site. A key problem is to identify this substructure, which can then be used as a pattern to screen databases of molecules. This problem is complicated by the fact that a drug molecule is flexible and can achieve many low-energy (stable) states. We present search techniques that we have developed to find these low-energy states and to identify common 3D substructures that appear in at least one low-energy state of most molecules in a given collection. We also show experimental results obtained with a software system implementing these techniques.