Virtual chemical reactions for drug design

Two methods for the fast, fragment-based combinatorial molecule assembly were developed. The software COLIBREE® (Combinatorial Library Breeding) generates candidate structures from scratch, based on stochastic optimization [1]. Result structures of a COLIBREE design run are based on a fixed scaffold and variable linkers and side-chains. Linkers representing virtual chemical reactions and side-chain building blocks obtained from pseudo-retrosynthetic dissection of large compound databases are exchanged during optimization. The process of molecule design employs a discrete version of Particle Swarm Optimization (PSO) [2]. Assembled compounds are scored according to their similarity to known reference ligands. Distance to reference molecules is computed in the space of the topological pharmacophore descriptor CATS [3]. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor (PPAR gamma) selective agonists. In a second approach, we developed the formal grammar Reaction-MQL [4] for the in silico representation and application of chemical reactions. Chemical transformation schemes are defined by functional groups participating in known organic reactions. The substructures are specified by the linear Molecular Query Language (MQL) [5]. The developed software package contains a parser for Reaction-MQL-expressions and enables users to design, test and virtually apply chemical reactions. The program has already been used to create combinatorial libraries for virtual screening studies. It was also applied in fragmentation studies with different sets of retrosynthetic reactions and various compound libraries

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

The efficiency of multi-target drugs: the network approach might help drug design

Despite considerable progress in genome- and proteome-based high-throughput screening methods and rational drug design, the number of successful single target drugs did not increase appreciably during the past decade. Network models suggest that partial inhibition of a surprisingly small number of targets can be more efficient than the complete inhibition of a single target. This and the success stories of multi-target drugs and combinatorial therapies led us to suggest that systematic drug design strategies should be directed against multiple targets. We propose that the final effect of partial, but multiple drug actions might often surpass that of complete drug action at a single target. The future success of this novel drug design paradigm will depend not only on a new generation of computer models to identify the correct multiple hits and their multi-fitting, low-affinity drug candidates but also on more efficient in vivo testing.Comment: 6 pages, 2 figures, 1 box, 38 reference

Controlling platinum, ruthenium, and osmium reactivity for anticancer drug design

The main task of the medicinal chemist is to design molecules that interact specifically with derailed or degenerating processes in a diseased organism, translating the available knowledge of pathobiochemical and physiological data into chemically useful information and structures. Current knowledge of the biological and chemical processes underlying diseases is vast and rapidly expanding. In particular the unraveling of the genome in combination with, for instance, the rapid development of structural biology has led to an explosion in available information and identification of new targets for chemotherapy. The task of translating this wealth of data into active and selective new drugs is an enormous, but realistic, challenge. It requires knowledge from many different fields, including molecular biology, chemistry, pharmacology, physiology, and medicine and as such requires a truly interdisciplinary approach. Ultimately, the goal is to design molecules that satisfy all the requirements for a candidate drug to function therapeutically. Therapeutic activity can then be achieved by an understanding of and control over structure and reactivity of the candidate drug through molecular manipulation

Simple models of protein folding and of non--conventional drug design

While all the information required for the folding of a protein is contained in its amino acid sequence, one has not yet learned how to extract this information to predict the three--dimensional, biologically active, native conformation of a protein whose sequence is known. Using insight obtained from simple model simulations of the folding of proteins, in particular of the fact that this phenomenon is essentially controlled by conserved (native) contacts among (few) strongly interacting ("hot"), as a rule hydrophobic, amino acids, which also stabilize local elementary structures (LES, hidden, incipient secondary structures like $\alpha$--helices and $\beta$--sheets) formed early in the folding process and leading to the postcritical folding nucleus (i.e., the minimum set of native contacts which bring the system pass beyond the highest free--energy barrier found in the whole folding process) it is possible to work out a succesful strategy for reading the native structure of designed proteins from the knowledge of only their amino acid sequence and of the contact energies among the amino acids. Because LES have undergone millions of years of evolution to selectively dock to their complementary structures, small peptides made out of the same amino acids as the LES are expected to selectively attach to the newly expressed (unfolded) protein and inhibit its folding, or to the native (fluctuating) native conformation and denaturate it. These peptides, or their mimetic molecules, can thus be used as effective non--conventional drugs to those already existing (and directed at neutralizing the active site of enzymes), displaying the advantage of not suffering from the uprise of resistance

Sequential Decision-Making for Drug Design: Towards closed-loop drug design

Drug design is a process of trial and error to design molecules with a desired response toward a biological target, with the ultimate goal of finding a new medication. It is estimated to be up to 10^{60} molecules that are of potential interest as drugs, making it a difficult problem to find suitable molecules. A crucial part of drug design is to design and determine what molecules should be experimentally tested, to determine their activity toward the biological target. To experimentally test the properties of a molecule, it has to be successfully made, often requiring a sequence of reactions to obtain the desired product. Machine learning can be utilized to predict the outcome of a reaction, helping to find successful reactions, but requires data for the reaction type of interest. This thesis presents a work that combinatorially investigates the use of active learning to acquire training data for reaching a certain level of predictive ability in predicting whether a reaction is successful or not. However, only a limited number of molecules can often be synthesized every time. Therefore, another line of work in this thesis investigates which designed molecules should be experimentally tested, given a budget of experiments, to sequentially acquire new knowledge. This is formulated as a multi-armed bandit problem and we propose an algorithm to solve this problem. To suggest potential drug molecules to choose from, recent advances in machine learning have also enabled the use of generative models to design novel molecules with certain predicted properties. Previous work has formulated this as a reinforcement learning problem with success in designing and optimizing molecules with drug-like properties. This thesis presents a systematic comparison of different reinforcement learning algorithms for string-based generation of drug molecules. This includes a study of different ways of learning from previous and current batches of samples during the iterative generation