A genetic algorithm for structure-based de novo design

Genetic algorithms have properties which make them attractive in de novo drug design. Like other de novo design programs, genetic algorithms require a method to reduce the enormous search space of possible compounds. Most often this is done using information from known ligands. We have developed the ADAPT program, a genetic algorithm which uses molecular interactions evaluated with docking calculations as a fitness function to reduce the search space. ADAPT does not require information about known ligands. The program takes an initial set of compounds and iteratively builds new compounds based on the fitness scores of the previous set of compounds. We describe the particulars of the ADAPT algorithm and its application to three well-studied target systems. We also show that the strategies of enhanced local sampling and re-introducing diversity to the compound population during the design cycle provide better results than conventional genetic algorithm protocols.     

Genetic algorithm for the design of molecules with desired properties

The design of molecules with desired properties is still a challenge because of the largely unpredictable end results. Computational methods can be used to assist and speed up this process. In particular, genetic algorithms have proved to be powerful tools with a wide range of applications, e.g. in the field of drug development. Here, we propose a new genetic algorithm that has been tailored to meet the demands of de novo drug design, i.e. efficient optimization based on small training sets that are analyzed in only a small number of design cycles. The efficiency of the design algorithm was demonstrated in the context of several different applications. First, RNA molecules were optimized with respect to folding energy. Second, a spinglass was optimized as a model system for the optimization of multiletter alphabet biopolymers such as peptides. Finally, the feasibility of the computer-assisted molecular design approach was demonstrated for the de novo construction of peptidic thrombin inhibitors using an iterative process of 4 design cycles of computer-guided optimization. Synthesis and experimental fitness determination of only 600 different compounds from a virtual library of more than 10¹⁷ molecules was necessary to achieve this goal.These authors contributed equally to the results presentedThese authors contributed equally to the results presentedThese authors contributed equally to the results presentedThese authors contributed equally to the results presented     

The use of quantum molecular calculations to guide a genetic algorithm: a way to search for new chemistry

The process of gene-based molecular evolution has been simulated in silico by using massively parallel density functional theory quantum calculations, coupled with a genetic algorithm, to test for fitness with respect to a target chemical reaction in populations of genetically encoded molecules. The goal of this study was the identification of transition-metal complexes capable of mediating a known reaction, namely the cleavage of N(2) to give the metal nitride. Each complex within the search space was uniquely specified by a nanogene consisting of an eight-digit number. Propagation of an individual nanogene into successive generations was determined by the fitness of its phenotypic molecule to perform the target reaction and new generations were created by recombination and mutation of surviving nanogenes. In its simplest implementation, the quantum-directed genetic algorithm (QDGA) quickly located a local minimum on the evolutionary fitness hypersurface, but proved incapable of progressing towards the global minimum. A strategy for progressing beyond local minima consistent with the Darwinian paradigm by the use of environmental variations coupled with mass extinctions was therefore developed. This allowed for the identification of nitriding complexes that are very closely related to known examples from the chemical literature. Examples of mutations that appear to be beneficial at the genetic level but prove to be harmful at the phenotypic level are described. As well as revealing fundamental aspects of molecular evolution, QDGA appears to be a powerful tool for the identification of lead compounds capable of carrying out a target chemical reaction.     

An evolutionary algorithm for de novo optimization of functional transition metal compounds

Development of functional inorganic and transition metal compounds is usually based on ad hoc qualified guesses, with computational methods playing a lesser role than in drug discovery. A de novo evolutionary algorithm (EA) is presented that automatically generates transition metal complexes using a search space constrained around chemically meaningful structures assembled from three kinds of fragments: a part shared by all structures and typically containing the metal center itself, one or several parts consisting of ligand skeletons, and unconstrained parts that may grow and vary freely. In EA optimizations, using a cost-efficient fitness function based on a linear quantitative structure-activity relationship model for catalytic activity, we demonstrate the capabilities of the method by retracing the transition from the first-generation, phosphine-based Grubbs olefin metathesis catalysts to second-generation catalysts containing N-heterocyclic carbene ligands instead of phosphines. Moreover, DFT calculations on selected high-fitness, last-generation structures from these evolutionary experiments suggest that, in terms of catalytic activity, the structures arrived at by virtual evolution alone compare favorably with existing, highly active catalysts. The structures from the evolution experiments are, however, complex and probably difficult to synthesize, but a set of manually simplified variations thereof might form the leads for a new generation of Grubbs catalysts.     

PRO_LIGAND: An approach to de novo molecular design. 3. A genetic algorithm for structure refinement

Summary Recently, the development of computer programs which permit the de novo design of molecular structures satisfying a set of steric and chemical constraints has become a burgeoning area of research and many operational systems have been reported in the literature. Experience with PRO_LIGAND—the de novo design methodology embodied in our in-house molecular design and simulation system PRO-METHEUS—has suggested that the addition of a genetic algorithm (GA) structure refinement procedure can add value to an already useful tool. Starting with the set of designed molecules as an initial population, the GA can combine features from both high- and low-scoring structures and, over a number of generations, produce individuals of better score than any of the starting structures. This paper describes how we have implemented such a procedure and demonstrates its efficacy in improving two sets of molecules generated by different de novo design projects.     

De novo ligand design to partially flexible active sites: application of the ReFlex algorithm to carboxypeptidase A, acetylcholinesterase, and the estrogen receptor

Reflex is a recent algorithm in the de novo ligand design software, SkelGen, that allows the flexibility of amino acid side chains in a protein to be taken into account during the drug-design process. In this paper the impact of flexibility on the solutions generated by the de novo design algorithm, when applied to carboxypeptidase A, acetylcholinesterase, and the estrogen receptor (ER), is investigated. The results for each of the targets indicate that when allowing side-chain movement in the active site, solutions are generated that were not accessible from the multiple static protein conformations available for these targets. Furthermore, an analysis of structures generated in a flexible versus a static ER active site suggests that these additional solutions are not merely noise but contain many interesting chemotypes.     

Automatic tailoring and transplanting: a practical method that makes virtual screening more useful

Docking-based virtual screening of large compound libraries has been widely applied to lead discovery in structure-based drug design. However, subsequent lead optimizations often rely on other types of computational methods, such as de novo design methods. We have developed an automatic method, namely automatic tailoring and transplanting (AutoT&T), which can effectively utilize the outcomes of virtual screening in lead optimization. This method detects suitable fragments on virtual screening hits and then transplants them onto a lead compound to generate new ligand molecules. Binding affinities, synthetic feasibilities, and drug-likeness properties are considered in the selection of final designs. In this study, our AutoT&T program was tested on three different target proteins, including p38 MAP kinase, PPAR-α, and Mcl-1. In the first two cases, AutoT&T was able to produce molecules identical or similar to known inhibitors with better potency than the given lead compound. In the third case, we demonstrated how to apply AutoT&T to design novel ligand molecules from scratch. Compared to the solutions generated by other two de novo design methods, i.e., LUDI and EA-Inventor, the solutions generated by AutoT&T were structurally more diverse and more promising in terms of binding scores in all three cases. AutoT&T also completed the assigned jobs more efficiently than LUDI and EA-Inventor by several folds. Our AutoT&T method has certain technical advantages over de novo design methods. Importantly, it expands the application of virtual screening from lead discovery to lead optimization and thus may serve as a valuable tool for many researchers.     

Designing the molecular future

Approximately 25 years ago the first computer applications were conceived for the purpose of automated ‘de novo’ drug design, prominent pioneering tools being ALADDIN, CAVEAT, GENOA, and DYLOMMS. Many of these early concepts were enabled by innovative techniques for ligand-receptor interaction modeling like GRID, MCSS, DOCK, and CoMFA, which still provide the theoretical framework for several more recently developed molecular design algorithms. After a first wave of software tools and groundbreaking applications in the 1990s—expressly GROW, GrowMol, LEGEND, and LUDI representing some of the key players—we are currently witnessing a renewed strong interest in this field. Innovative ideas for both receptor and ligand-based drug design have recently been published. We here provide a personal perspective on the evolution of de novo design, highlighting some of the historic achievements as well as possible future developments of this exciting field of research, which combines multiple scientific disciplines and is, like few other areas in chemistry, subject to continuous enthusiastic discussion and compassionate dispute.     

Automated de novo sequencing of nucleic acids by liquid chromatography-tandem mass spectrometry

We present the first global computer-aided sequencing algorithm for the de novo determination of short nucleic acid sequences. The method compares the fragment ion spectra generated by collision-induced dissociation of multiply charged oligodeoxynucleotide-ions to the m/z values predicted employing established fragmentation pathways from a known reference sequence. The closeness of matching between the measured spectrum and the predicted set of fragment ions is characterized by the fitness, which takes into account the difference between measured and predicted m/z values, the intensity of the fragment ions, the number of fragments assigned, and the number of nucleotide positions not covered by fragment ions in the experimental spectrum. Smaller values for the fitness indicate a closer match between the measured spectrum and predicted m/z values. In order to find the sequence most closely matching the experimental spectrum, starting from a given nucleotide composition all possible oligonucleotide sequences are assembled followed by identification of the correct sequence by the lowest fitness value. Using this concept, sequences of 5- to 12-mer oligodeoxynucleotides were successfully de novo determined. High sequence coverage with fragment ions was essential for obtaining unequivocal sequencing results. Moreover, the collision energy was shown to have an impact on the interpretability of tandem mass spectra by the de novo sequencing algorithm. Experiments revealed that the optimal collision energy should be set to a value just sufficient for complete fragmentation of the precursor ion.     

Engineering of biocatalysts - from evolution to creation

Enzymes are increasingly being used in an industrial setting as a cheap and environmentally-friendly alternative to chemical catalysts. In order to produce the ideal biocatalyst, natural enzymes often require optimization to increase their catalytic efficiencies and specificities under a particular range of reaction conditions. A number of enzyme engineering strategies are currently employed to modify biocatalysts, improving their suitability for large-scale industrial applications. These include various directed evolution techniques, semi-rational design techniques, and more recently, the de novo design of novel enzymes. Advances in mutant library design, high-throughput selection processes, and the introduction of powerful computer algorithms have all contributed to the current exponential growth of the field of enzyme engineering. This review article aims to present some of the currently employed strategies for enzyme engineering and attempts to highlight the most recent advances in methodology.     

Exhaustive de novo design of low-molecular-weight fragments against the ATP-binding site of DNA-gyrase

We present a de novo design approach to generating small fragments in the DNA-gyrase ATP-binding site using the computational drug design platform SkelGen. We have generated an exhaustive number of structural possibilities, which were subsequently filtered for site complementarity and synthetic tractability. A number of known active fragments are found, but most of the species created are potentially novel and could be valuable for further elaboration and development into lead-like structures.     

HostDesigner: a program for the de novo structure-based design of molecular receptors with binding sites that complement metal ion guests

This paper describes a novel approach to the discovery of host structures with binding sites that complement targeted metal ion guests. This approach uses a de novo structure-based design strategy that couples molecular building algorithms with scoring functions to prioritize candidate structures. The algorithms described herein have been implemented in a program called HostDesigner, the first structure-based design software specifically created for the discovery of metal ion receptors. HostDesigner generates and evaluates millions of candidate structures within minutes, rapidly identifying three-dimensional architectures that position binding sites to provide an optimal interaction with the metal ion.     

CONFIRM: connecting fragments found in receptor molecules

A novel algorithm for the connecting of fragment molecules is presented and validated for a number of test systems. Within the CONFIRM (Connecting Fragments Found in Receptor Molecules) approach a pre-prepared library of bridges is searched to extract those which match a search criterion derived from known experimental or computational binding information about fragment molecules within a target binding site. The resulting bridge 'hits' are then connected, in an automated fashion, to the fragments and docked into the target receptor. Docking poses are assessed in terms of root-mean-squared deviation from the known positions of the fragment molecules, as well as docking score should known inhibitors be available. The creation of the bridge library, the full details and novelty of the CONFIRM algorithm, and the general applicability of this approach within the field of fragment-based de novo drug design are discussed.     

The molecule evoluator. An interactive evolutionary algorithm for the design of drug-like molecules

We developed a software tool to design drug-like molecules, the "Molecule Evoluator", which we introduce and describe here. An atom-based evolutionary approach was used allowing both several types of mutation and crossover to occur. The novelty, we claim, is the unprecedented interactive evolution, in which the user acts as a fitness function. This brings a human being's creativity, implicit knowledge, and imagination into the design process, next to the more standard chemical rules. Proof-of-concept was demonstrated in a number of ways, both computationally and in the lab. Thus, we synthesized a number of compounds designed with the aid of the Molecule Evoluator. One of these is described here, a new chemical entity with activity on alpha-adrenergic receptors.     

遗传算法在构象搜寻中的应用

遗传算法是一种模仿自然进化进程的新颖的启发式优化方法。通过它，人们可以在计算机上对所需的各种问题实施进化操作，最终产生理想的结果。遗传算法现已被广泛于计算机辅助设计、工程设计、系统模拟等领域并取得了极大的成功。本文拟对遗传算法在对大、中、小分子的构象搜寻中的应用作一较全面的综述。     

Combinatorial libraries of biocatalysts: application and screening

Enzymes can perform intricate regioselective and/or enantioselective chemical transformations and can accelerate reaction rates by enormous factors all under mild conditions. However, enzymes almost always present problems for use on an industrial scale. Evolutionary design approaches can be applied to the generation of stable enzymes with improved or novel catalytic activities. Directed evolution can be considered as the biotechnological equivalent of combinatorial chemistry, where the expressed proteins are the combinatorial libraries of biocatalysts. This review will focus on the search of novel biocatalysts produced by genetic engineering with modified activity and stability in different environments, substrate specificity and enantioselectivity. Methods of screening and/or selection for the desired properties will also be described. Finally, the efforts in de novo enzyme design are mentioned.