How Computer systems “See” Molecules | In the direction of Information Science

a pc, Edvard Munch’s The Scream is nothing greater than a grid of pixel values. It has no sense of why swirling traces in a twilight sky convey the agony of a scream. That’s as a result of (fashionable digital) computer systems basically course of solely binary alerts [1,2]; they don’t inherently comprehend the objects and feelings we understand.

To imitate human intelligence, we first want an intermediate kind (illustration) to “translate” our sensory world into one thing a pc can deal with. For The Scream, that may imply extracting edges, colours, shapes, and so forth. Likewise, in Pure Language Processing (NLP), a pc sees human language as an unstructured stream of symbols that have to be became numeric vectors or different structured types. Solely then can it start to map uncooked enter to higher-level ideas (i.e., constructing a mannequin).

Human intelligence additionally depends upon inner representations.

In psychology, a illustration refers to an inner psychological image or picture that stands for one thing within the outdoors world [3]. In different phrases, a illustration is how data is encoded within the mind: the symbols we use (phrases, pictures, reminiscences, inventive depictions, and so forth.) to face for objects and concepts.

Our senses don’t merely put the exterior world straight into our brains; as a substitute, they convert sensory enter into summary neural alerts. For instance, the eyes convert gentle into electrical alerts on the retina, and the ears flip air vibrations into nerve impulses. These neural alerts are the mind’s illustration of the exterior world, which is used to reconstruct our notion of actuality, basically constructing a “mannequin” in our thoughts.

Between ages one and two, kids enter Piaget’s early preoperational stage [4]. That is when youngsters begin utilizing one factor to signify one other: a toddler would possibly maintain a banana as much as their ear and babble as if it’s a telephone, or push a field round pretending it’s a automotive. This type of symbolic play is necessary for cognitive growth, as a result of it reveals the kid can transfer past the here-and-now and mission the ideas of their thoughts onto actuality [5].

With out our senses translating bodily alerts into inner codes, we couldn’t understand something [5].

“Rubbish in, rubbish out”. The standard of a illustration units an higher certain on the efficiency of any mannequin constructed on it [6,7].

A lot of the progress in human intelligence has come from enhancing how we signify data [8].

One of many core objectives of training is to assist college students kind efficient psychological representations of latest data. Seasoned educators use diagrams, animations, analogies and different instruments to current summary ideas in a vivid, relatable manner. Richard Mayer argues that significant studying occurs when learners kind a coherent psychological illustration or mannequin of the fabric, relatively than simply memorizing disconnected info [8]. In significant studying, new data integrates into current data, permitting college students to switch and apply it in novel conditions.

Nonetheless, in observe, components like restricted mannequin capability and finite computing assets constrain how complicated our representations could be. Compressing enter knowledge inevitably dangers data loss, noise, and artifacts. So, as step one, growing a “ok” illustration requires balancing a number of key properties:

• It ought to retain the data important to the duty. (A clear drawback definition helps filter out the remaining.)
• It ought to be as compact as potential: minimizing redundancy and conserving dimensionality low.
• It ought to separate courses in characteristic area. Samples from the identical class cluster collectively, whereas these from completely different courses keep far aside.
• It ought to be sturdy to enter noise, compression artifacts, and shifts in knowledge modality.
• Invariance. Representations ought to be invariant to job‑irrelevant modifications (e.g. rotating or translating a picture, or altering its brightness).
• Generalizability.
• Interpretability.
• Transferability.

These limitations on illustration complexity are considerably analogous to the restricted capability of our personal working reminiscence.

Human short-term reminiscence, on common, can solely maintain about 7±2 objects directly [9]. When too many unbiased items of data arrive concurrently (past what our cognitive load can deal with), our brains lavatory down. Cognitive psychology analysis reveals that with the suitable steering (by adjusting how data is represented), individuals can reorganize data to beat this obvious restrict [10,11]. For instance, we will keep in mind an extended string of digits extra simply by chunking them into significant teams (which is why telephone numbers are sometimes cut up into shorter blocks).

Now, shifting from The Scream to the microscopic world of molecules, we face the identical problem: how can we translate real-world molecules right into a kind that a pc can perceive? With the suitable illustration, a pc can infer chemical properties or organic features, and in the end map these to larger‑degree ideas (e.g., a drug’s exercise or a molecule’s protein binding). On this article, we’ll discover the frequent strategies that allow computer systems “see” molecules.

Chemical System

Maybe probably the most simple depiction of a molecule is its chemical components, like C₈H₁₀N₄O₂ (caffeine), which tells us there are 8 carbon atoms, 10 hydrogen atoms, 4 nitrogen atoms and a couple of oxygen atoms. Nonetheless, its very simplicity can also be its limitation: a components conveys nothing about how these atoms are related (the bonding topology), how they’re organized in area, or the place purposeful teams are positioned. That’s why isomers (like ethanol and dimethyl ether) each share C₂H₆O but differ utterly in construction and properties.

Linear String

One other frequent strategy to signify molecules is to encode them as a linear string of characters, a format broadly adopted in databases [12,13].

SMILES

Probably the most traditional instance is SMILES (Simplified Molecular Enter Line Entry System) [14], developed by David Weininger within the Eighties. SMILES treats atoms as nodes and bonds as edges, then “flattens” them right into a 1D string by way of a depth‑first traversal, preserving all of the connectivity and ring data. Single, double, triple, and fragrant bonds are denoted by the symbols “-”, “=”, “#”, and “:”, respectively. Numbers are used to mark the beginning and finish of rings, and branches off the primary chain are enclosed in parentheses. (See extra in SMILES – Wikipedia.)

SMILES is easy, intuitive, and compact for storage. Its prolonged syntax helps stereochemistry and isotopes. There may be additionally a wealthy ecosystem of instruments supporting it: most chemistry libraries allow us to convert between SMILES and different commonplace codecs.

Nonetheless, with out an agreed-upon canonicalization algorithm, the identical molecule could be written in a number of legitimate SMILES types. This will probably result in inconsistencies or “knowledge air pollution”, particularly when merging knowledge from a number of sources.

InChI

One other broadly used string format is InChI (Worldwide Chemical Identifier) [15], launched by IUPAC in 2005, to generate globally standardized, machine-readable, and distinctive molecule identifiers. InChI strings, although longer than SMILES, encode extra particulars in layers (together with atoms and their bond connectivity, tautomeric state, isotopes, stereochemistry, and cost), every with strict guidelines and precedence. (See extra in InChI – Wikipedia.)

As a result of an InChI string can turn out to be very prolonged as a molecule grows extra complicated, it’s typically paired with a 27‑character InChIKey hash [15]. The InChIKeys aren’t human‑pleasant, however they’re superb for database indexing and for exchanging molecule identifiers throughout methods.

Molecular Descriptor

Many computational fashions require numeric inputs. In comparison with linear string representations, molecular descriptors flip a molecule’s properties and patterns into a vector of numerical options, delivering passable efficiency in lots of duties [7, 16-18].

Todeschini and Consonni describe the molecular descriptor because the “last results of a logical and mathematical process, which transforms chemical data encoded inside a symbolic illustration of a molecule right into a helpful quantity or the results of some standardized experiment” [16].

We are able to consider a set of molecular descriptors as a standardized “bodily examination sheet” for a molecule, asking questions like:

• Does it have a benzene ring?
• What number of carbon atoms does it have?
• What’s the expected octanol-water partition coefficient (LogP)?
• Which purposeful teams are current?
• What’s its 3D conformation or electron distribution like?
• …

Their solutions can take numerous types, resembling numerical values, categorical flags, vectors, graph-based buildings, tensors and so forth. As a result of each molecule in our dataset is described utilizing the identical set of questions (the identical “bodily examination sheet”), comparisons and mannequin inputs turn out to be simple. And since every characteristic has a transparent which means, descriptors enhance the interpretability of the mannequin.

After all, simply as a bodily examination sheet can’t seize completely all the pieces about an individual’s well being, a finite set of molecular descriptors can by no means seize all points of a molecule’s chemical and bodily nature. Computing descriptors is often a non-invertible course of, inevitably resulting in a lack of data, and the outcomes should not assured to be distinctive. Due to this fact, there are several types of molecular descriptors, every specializing in completely different points.

Hundreds of molecular descriptors have been developed over time (for instance, RDKit [19], CDK [20], Mordred [17], and so forth.). They are often broadly categorized by the dimensionality of data they encode (these classes aren’t strict divisions):

• 0D: components‑primarily based properties unbiased of construction (e.g., atom counts or molecular weight).
• 1D: sequence-based properties (e.g., counts of sure purposeful teams).
• 2D: derived from the 2D topology (e.g., eccentric connectivity index [21]).
• 3D: derived from 3D conformation, capturing geometric or spatial properties (e.g., charged partial floor space [22]).
• 4D and better: these incorporate further dimensions resembling time, ensemble, or environmental components (e.g., descriptors derived from molecular dynamics simulations, or from quantum chemical calculations like HOMO/LUMO).
• Descriptors obtained from different sources together with experimental measurements.

Molecular fingerprints are a particular type of molecular descriptor that encode substructures right into a fixed-length numerical vector [16]. This desk summarizes some generally used molecular fingerprints [23], resembling MACCS [24], which is proven within the determine beneath.

Equally, human fingerprints or product barcodes can be seen as (or transformed to) fixed-format numerical representations.

Totally different descriptors describe molecules from numerous points, so their contributions to completely different duties naturally range. In a job of predicting the aqueous solubility of drug-like molecules, over 4,000 computed descriptors have been evaluated, however solely about 800 made important contributions to the prediction [7].

Level Cloud

Typically, we want our fashions to be taught straight from a molecule’s 3D construction. For instance, that is necessary once we’re concerned with how two molecules would possibly work together with one another [25], want to go looking the potential conformations of a molecule [26], or wish to simulate its conduct in a sure surroundings [27].

One simple strategy to signify a 3D construction is as some extent cloud of its atoms [28]. In different phrases, some extent cloud is a group of coordinates of the atoms in 3D area. Nonetheless, whereas this illustration reveals which atoms are close to one another, it doesn’t explicitly inform us which pairs of atoms are bonded. Inferring connectivity from interatomic distances (e.g., by way of cutoffs) could be error-prone, and will miss larger‑order chemistry like aromaticity or conjugation. Furthermore, our mannequin should account for modifications of uncooked coordinates attributable to rotation or translation. (Extra on this later.)

Graph

A molecule can be represented as a graph, the place atoms (nodes) are related by bonds (edges). Graph representations elegantly deal with rings, branches, and complicated bonding preparations. For instance, in a SMILES string, a benzene ring have to be “opened” and denoted by particular symbols, whereas in a graph, it’s merely a cycle of nodes related in a loop.

Molecules are generally modeled as undirected graphs (since bonds haven’t any inherent route) [29-31]. We are able to additional “enhance” the graph with further domain-specific data to make the illustration extra interpretable: tagging nodes with atom options (e.g., ingredient sort, cost, aromaticity) and edges with bond properties (e.g., order, size, energy). Due to this fact,

• (uniqueness) every distinct molecular construction may correspond to a singular graph, and
• (reversibility) we may reconstruct the unique molecule from its graph illustration.

Chemical reactions basically contain breaking bonds and forming new ones. Utilizing graphs makes it simpler to trace these modifications. Some response‑prediction fashions encode reactants and merchandise as graphs and infer the transformation by evaluating them [32,33].

Graph Neural Networks (GNNs) can straight course of graphs and be taught from them. Utilizing molecular graph illustration, these fashions can naturally deal with molecules of arbitrary dimension and topology. In reality, many GNNs have outperformed fashions that solely relied on descriptors or linear strings on many molecular duties [7,30,34].

Usually, when a GNN makes a prediction, we will examine which elements of the graph have been most influential. These “necessary bits” often correspond to precise chemical substructures or purposeful teams. In distinction, if we have been taking a look at a selected substring of a SMILES, it’s not assured to map neatly to a significant substructure.

A graph doesn’t at all times imply simply the direct bonds connecting atoms. We are able to assemble completely different sorts of graphs from molecular knowledge relying on our wants, and generally these alternate graphs yield higher outcomes for explicit purposes. For instance:

Full graph: Each pair of nodes is related by an edge. It may introduce redundant connections, however is likely to be used to let a mannequin contemplate all pairwise interactions.
Bipartite graph: Nodes are divided into two units, and edges solely join nodes from one set to nodes from the opposite.
Nearest-neighbor graph: Every node is related solely to its nearest neighbors (in response to some criterion), for controlling complexity.

Extensible Graph Representations

We are able to incorporate chemical guidelines or impose constraints inside molecular graphs. In de novo molecular design, (early) SMILES‑primarily based generative fashions typically produced SMILES strings ended up proposing invalid molecules, as a result of: (1) assembling characters could break SMILES syntax, and (2) even a syntactically right SMILES would possibly encode an unimaginable construction. Graph‑primarily based generative fashions keep away from them by constructing molecules atom by atom and bond by bond (below user-specified chemical guidelines). Graphs additionally allow us to impose constraints: require or forbid particular substructures, implement 3D shapes or chirality, and so forth; thus, to information technology towards legitimate candidates that meet our objectives [35,36].

Molecular graphs can even deal with a number of molecules and their interactions (e.g., drug-protein binding, protein-protein interfaces). “Graph-of-graphs” deal with every molecule as its personal graph, then deploy a higher-level mannequin to learn the way they work together [37]. Or, we could merge the molecules into one composite graph, together with all atoms from each companions and add particular (dummy) edges or nodes to mark their contacts [38].

Thus far, we’ve been contemplating the usual graph of bonds (the 2D connectivity), however what if the 3D association issues? Graph representations can actually be augmented with 3D data: 3D coordinates may very well be hooked up to every node, or distances/angles may very well be added as attributes on the sides, to make fashions extra delicate to distinction in 3D configurations. A greater possibility is to make use of fashions like SE(3)-equivariant GNNs, which guarantee their outputs (or key inner options) rework (or keep invariant) with any rotation or translation of the enter.

In 3D area, the particular Euclidean group SE(3) describes all potential inflexible motions (any mixture of rotations and translations). (It’s generally described as a semidirect product of the rotation group SO(3) with the interpretation group R³.) [28]

Once we say a mannequin or a operate has SE(3) invariance, we imply that it offers the identical consequence regardless of how we rotate or translate the enter in 3D. This type of invariance is usually a vital requirement for a lot of molecular modeling duties: a molecule floating in resolution has no fastened reference body (i.e., it could actually tumble round in area). So, if we predict some property of the molecule (say its binding affinity), that prediction shouldn’t be influenced by the molecule’s orientation or place.

Sequence Representations of Biomacromolecules

We’ve talked principally about small molecules. However organic macromolecules (like proteins, DNA, and RNA) can comprise 1000’s and even tens of millions of atoms. SMILES or InChI strings turn out to be extraordinarily lengthy and complicated, resulting in the related huge computational, storage, and evaluation prices.

This brings us again to the significance of defining the issue: for biomacromolecules, we’re typically not within the exact place of each single atom or the precise bonds between every pair of atoms. As a substitute, we care about higher-level structural patterns and purposeful modules: like a protein’s amino acid spine and its alpha‑helices or beta‑sheets, which fold into tertiary and quaternary buildings. For DNA and RNA, we could care about nucleotide sequences and motifs.

We describe these organic polymers as sequences of their constructing blocks (i.e., main construction): proteins as chains of amino acids, and DNA/RNA as strings of nucleotides. There are well-established codes for these constructing blocks (outlined by IUPAC/IUBMB): for example, in DNA, the letters A, C, G, T signify the bases adenine, cytosine, guanine, and thymine respectively.

Static Embeddings and Pretrained Embeddings

To transform a sequence into numerical vectors, we will use static embeddings: assigning a hard and fast vector to every residue (or k-mer fragment). The best static embedding is one-hot encoding (e.g., encode adenine A as [1,0,0,0]), turning a sequence right into a matrix. One other strategy is to be taught dense (pretrained) embeddings by leveraging massive databases of sequences. For instance, ProtVec [39] breaks proteins into overlapping 3‑mers and trains a Word2Vec‑like mannequin (generally utilized in NLP) on a big corpus of sequences, assigning every 3-mer a 100D vector. These discovered fragment embeddings are proven to seize biochemical and biophysical patterns: fragments with related features or properties cluster nearer within the embedding area.

k-mer fragments (or k-mers) are substrings of size ok extracted from a organic sequence.

Tokens

Impressed by NLP, we will deal with a sequence as if it’s a sentence composed of tokens or phrases (i.e., residues or k-mer fragments), after which feed them into deep language fashions. Skilled on huge collections of sequences, these fashions be taught biology’s “grammar” and “semantics” simply as they do in human language.

Transformers can use self‑consideration to seize lengthy‑vary dependencies in sequences; and we basically use them to be taught a “language of biology”. (Some) Meta’s ESM collection of fashions [40-42] skilled Transformers on tons of of tens of millions of protein sequences. Equally, DNABERT [43] tokenizes DNA into ok‑mers for BERT coaching on genomic knowledge. These sorts of obtained embeddings have been proven to encapsulate a wealth of organic data. In lots of circumstances, these embeddings can be utilized straight for numerous duties (i.e., switch studying).

Descriptors

In observe, sequence-based fashions typically mix their embeddings with physicochemical properties, statistical options, and different descriptors, resembling the share of every amino acid in a protein, the GC content material of a DNA sequence, or indices like hydrophobicity, polarity, cost, and molecular quantity.

Past the primary classes above, there are another unconventional methods to signify sequences. Chaos Sport Illustration (CGR) [44] maps DNA sequences to factors in a 2D airplane, creating distinctive picture patterns for downstream evaluation.

Structural Representations of Biomacromolecules

The complicated construction (of a protein) determines its features and specificities [28]. Merely understanding the linear sequence of residues is usually not sufficient to completely perceive a biomolecule’s operate or mechanism (i.e., sequence-structure hole).

Buildings are usually extra conserved than sequences [28, 45]. Two proteins may need very divergent sequences however nonetheless fold into extremely related 3D buildings [46]. Fixing the construction of a biomolecule can provide insights that we wouldn’t get simply from the sequence alone.

Granularity and Dimensionality Management

A single biomolecule could comprise on the order of 10³-10⁵ atoms (or much more). Encoding each atom and bond explicitly into numerical kind produces prohibitively high-dimensional, sparse representations.

Including dimensions to the illustration can shortly run into the curse of dimensionality. As we improve the dimensionality of our knowledge, the “area” we’re asking our mannequin to cowl grows exponentially. Information factors turn out to be sparser relative to that area (it’s like having just a few needles in an ever-expanding haystack). This sparsity means a mannequin would possibly want vastly extra coaching examples to search out dependable patterns. In the meantime, the computational value of processing the information typically grows polynomially or worse with dimensionality.

Not each atom is equally necessary for the query we care about: we regularly flip to regulate the granularity of our illustration or scale back dimensionality in good methods (such knowledge typically has a lower-dimensional efficient illustration that may describe the system with out (important) efficiency loss [47]):

• For proteins, every amino acid could be represented by the coordinates of simply its alpha carbon (C_α). For nucleic acids, one would possibly take every nucleotide and signify it by the place of its phosphate group or by the middle of its base or sugar ring.
• One other instance of managed granularity comes from how AlphaFold [49] represents protein utilizing spine inflexible teams (or frames). Basically, for every amino acid, a small set of main-chain atoms, usually the N, C_α, C (and possibly O) are handled as a unit. The relative geometry of those atoms is nearly fastened (covalent bond lengths and angles don’t range considerably), in order that unit could be thought of as a inflexible block. As a substitute of monitoring every atom individually, the mannequin tracks the place and orientation of that total block in area, decreasing the dangers related to extreme levels of freedom [28] (i.e., errors from the inner motion of atoms inside a residue).

• If we now have a big set of protein buildings (or an extended molecular dynamics trajectory), it may be helpful to cluster these conformations into just a few consultant states. That is typically completed when constructing Markov state fashions: by clustering steady states right into a finite set of discrete “metastable” states, we will simplify a fancy power panorama right into a community of some states related by transition chances.

Many coarse-grained molecular dynamics pressure fields, resembling MARTINI [50] and UNRES [51], have been developed to signify structural particulars utilizing fewer particles.

• To seize for side-chain results with out modelling all inner atoms or including extreme levels of freedom, a typical strategy is to signify every side-chain with a single level, usually its heart of mass [52]. Such side-chain centroid fashions are sometimes used along with spine fashions.
• The 3Di Alphabet launched by Foldseek [53] defines a 3D interplay “alphabet” of 20 states that describe protein tertiary interactions. Thus, a protein’s 3D construction could be transformed right into a sequence of 20 symbols; and two buildings could be aligned by aligning their 3Di sequences.
• We could spatially crop or give attention to simply a part of a biomolecule. As an example, if we’re finding out how a small drug molecule binds to a protein (say, in a dataset like PDBBind [54], which is filled with protein-ligand complexes), we could solely feed the pockets and medicines into our mannequin.
• Combining completely different granularities or modalities of knowledge.

Level Cloud

We may mannequin a biomacromolecule as a large 3D level cloud of each atom (or residue). As famous earlier, the identical limitations apply.

Distance Matrix

A distance matrix information all pairwise distances between sure key atoms (for proteins, generally the C_α of every amino acid), and is inherently invariant to rotation and translation attributable to its symmetric nature. A contact map simplifies this additional by indicating solely which pairs of residues are “shut sufficient” to keep in touch. Nonetheless, each representations lose directional data; so not all structural particulars could be recovered from them alone.

Graph

Equally, similar to we will use graphs for small molecules, we will use graphs for macromolecular buildings [55,56]. As a substitute of atoms, every node would possibly signify a bigger unit (see Granularity and Dimensionality Management). To enhance interpretability, further data like residue descriptors and recognized interplay networks inside a protein, can also be included in nodes and edges. Observe that the graph illustration for biomacromolecules inherits lots of the benefits we mentioned for small molecules.

For macromolecules, edges are sometimes pruned to maintain the graph sparse and manageable in dimension: basically a type of native magnification that focuses on native substructures, whereas far-apart relationships are handled as background context.

Basic dimensionality discount strategies resembling PCA, t-SNE and UMAP are additionally broadly used to investigate the high-dimensional structural knowledge of macromolecules. Whereas they don’t give us representations for computation in the identical sense because the others we’ve mentioned, they assist mission complicated knowledge into decrease dimensions (e.g., for visualization or insights).

Latent Area

Once we prepare a mannequin (particularly generative fashions), it typically learns to encode knowledge right into a compressed inner illustration. This inner illustration lives in some area of decrease dimension, referred to as the latent area. Consider London’s complicated city format, dense and complicated, whereas the latent area is sort of a “map” that captures its essence in a simplified kind.

Latent areas are normally in a roundabout way interpretable, however we will discover them by seeing how modifications in latent variables map to modifications within the output. In molecular technology, if a mannequin maps molecules right into a latent area, we will take two molecules (say, as two factors in that area) and generate a path between them. Ochiai et. al. [57] did this by taking two recognized molecules as endpoints, interpolating between their latent representations, and decoding the intermediate factors. The consequence was a set of latest molecules that blended options of each originals: hybrids that may have combined properties of the 2.

—— About Creator ——

Tianyuan Zheng
[email protected] | [email protected]
Computational Biology, Bioinformatics, Synthetic Intelligence

Division of Pc Science and Know-how
Division of Utilized Arithmetic and Theoretical Physics
College of Cambridge

Reference

1. Patterson DA, Hennessy JL. Pc group and design ARM version: the {hardware} software program interface. Morgan kaufmann; 2016 Could 6.
2. Harris S, Harris D. Digital Design and Pc Structure, RISC-V Version. Morgan Kaufmann; 2021 Jul 12.
3. Kosslyn SM, Koenig O. Moist thoughts: The brand new cognitive neuroscience. Simon and Schuster; 1992.
4. Piaget J, Prepare dinner M. The origins of intelligence in kids. New York: Worldwide universities press; 1952.
5. Bergen D. The function of faux play in kids’s cognitive growth. Early Childhood Analysis & Follow. 2002;4(1):n1.
6. Bengio Y, Courville A, Vincent P. Illustration studying: A assessment and new views. IEEE transactions on sample evaluation and machine intelligence. 2013 Mar 7;35(8):1798-828.
7. Zheng T, Mitchell JB, Dobson S. Revisiting the appliance of machine studying approaches in predicting aqueous solubility. ACS omega. 2024 Jul 31;9(32):35209-22.
8. Mayer RE. Multimedia studying. In Psychology of studying and motivation 2002 Jan 1 (Vol. 41, pp. 85-139). Tutorial Press.
9. Miller GA. The magical quantity seven, plus or minus two: Some limits on our capability for processing data. Psychological assessment. 1956 Mar;63(2):81.
10. Chase WG, Simon HA. Notion in chess. Cognitive psychology. 1973 Jan 1;4(1):55-81.
11. Simon HA. How Large Is a Chunk? By combining knowledge from a number of experiments, a fundamental human reminiscence unit could be recognized and measured. Science. 1974 Feb 8;183(4124):482-8.
12. Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L. PubChem 2025 replace. Nucleic acids analysis. 2025 Jan 6;53(D1):D1516-25.
13. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The protein knowledge financial institution. Nucleic acids analysis. 2000 Jan 1;28(1):235-42.
14. Weininger D. SMILES, a chemical language and knowledge system. 1. Introduction to methodology and encoding guidelines. Journal of chemical data and pc sciences. 1988 Feb 1;28(1):31-6.
15. Heller S, McNaught A, Stein S, Tchekhovskoi D, Pletnev I. InChI-the worldwide chemical construction identifier commonplace. Journal of cheminformatics. 2013 Jan 24;5(1):7.
16. Todeschini R, Consonni V. Molecular descriptors for chemoinformatics: quantity I: alphabetical itemizing/quantity II: appendices, references. John Wiley & Sons; 2009 Oct 30.
17. Moriwaki H, Tian YS, Kawashita N, Takagi T. Mordred: a molecular descriptor calculator. Journal of cheminformatics. 2018 Feb 6;10(1):4.
18. Jaganathan Ok, Tayara H, Chong KT. An explainable supervised machine studying mannequin for predicting respiratory toxicity of chemical compounds utilizing optimum molecular descriptors. Pharmaceutics. 2022 Apr 11;14(4):832.
19. RDKit: Open-source cheminformatics. https://www.rdkit.org
20. Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N, Kuhn S, Pluskal T, Rojas-Chertó M, Spjuth O, Torrance G. The Chemistry Growth Equipment (CDK) v2. 0: atom typing, depiction, molecular formulation, and substructure looking out. Journal of cheminformatics. 2017 Jun 6;9(1):33.
21. Sharma V, Goswami R, Madan AK. Eccentric connectivity index: A novel extremely discriminating topological descriptor for construction− property and construction− exercise research. Journal of chemical data and pc sciences. 1997 Mar 24;37(2):273-82.
22. Stanton DT, Jurs PC. Growth and use of charged partial floor space structural descriptors in computer-assisted quantitative structure-property relationship research. Analytical Chemistry. 1990 Nov 1;62(21):2323-9.
23. Boldini D, Ballabio D, Consonni V, Todeschini R, Grisoni F, Sieber SA. Effectiveness of molecular fingerprints for exploring the chemical area of pure merchandise. Journal of Cheminformatics. 2024 Mar 25;16(1):35.
24. Durant JL, Leland BA, Henry DR, Nourse JG. Reoptimization of MDL keys to be used in drug discovery. Journal of chemical data and pc sciences. 2002 Nov 25;42(6):1273-80.
25. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in digital screening for drug discovery: strategies and purposes. Nature evaluations Drug discovery. 2004 Nov 1;3(11):935-49.
26. Friedrich NO, Meyder A, de Bruyn Kops C, Sommer Ok, Flachsenberg F, Rarey M, Kirchmair J. Excessive-quality dataset of protein-bound ligand conformations and its software to benchmarking conformer ensemble mills. Journal of chemical data and modeling. 2017 Mar 27;57(3):529-39.
27. Karplus M, McCammon JA. Molecular dynamics simulations of biomolecules. Nature structural biology. 2002 Sep 1;9(9):646-52.
28. Zheng T, Rondina A, Micklem G, Lio P. Challenges and Pointers in Deep Generative Protein Design: 4 Case Research.
29. Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP. Convolutional networks on graphs for studying molecular fingerprints. Advances in neural data processing methods. 2015;28.
30. Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE. Neural message passing for quantum chemistry. InInternational convention on machine studying 2017 Jul 17 (pp. 1263-1272). Pmlr.
31. Wu Z, Pan S, Chen F, Lengthy G, Zhang C, Yu PS. A complete survey on graph neural networks. IEEE transactions on neural networks and studying methods. 2020 Mar 24;32(1):4-24.
32. Jin W, Coley C, Barzilay R, Jaakkola T. Predicting natural response outcomes with weisfeiler-lehman community. Advances in neural data processing methods. 2017;30.
33. Shi C, Xu M, Guo H, Zhang M, Tang J. A graph to graphs framework for retrosynthesis prediction. InInternational convention on machine studying 2020 Nov 21 (pp. 8818-8827). PMLR.
34. Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, Leswing Ok, Pande V. MoleculeNet: a benchmark for molecular machine studying. Chemical science. 2018;9(2):513-30.
35. Lim J, Ryu S, Kim JW, Kim WY. Molecular generative mannequin primarily based on conditional variational autoencoder for de novo molecular design. Journal of cheminformatics. 2018 Jul 11;10(1):31.
36. Maziarka Ł, Pocha A, Kaczmarczyk J, Rataj Ok, Danel T, Warchoł M. Mol-CycleGAN: a generative mannequin for molecular optimization. Journal of Cheminformatics. 2020 Jan 8;12(1):2.
37. Wang H, Lian D, Zhang Y, Qin L, Lin X. Gognn: Graph of graphs neural community for predicting structured entity interactions. arXiv preprint arXiv:2005.05537. 2020 Could 12.
38. Jiang D, Hsieh CY, Wu Z, Kang Y, Wang J, Wang E, Liao B, Shen C, Xu L, Wu J, Cao D. InteractionGraphNet: a novel and environment friendly deep graph illustration studying framework for correct protein–ligand interplay predictions. Journal of medicinal chemistry. 2021 Dec 8;64(24):18209-32.
39. Asgari E, Mofrad MR. Steady distributed illustration of organic sequences for deep proteomics and genomics. PloS one. 2015 Nov 10;10(11):e0141287.
40. Rives A, Meier J, Sercu T, Goyal S, Lin Z, Liu J, Guo D, Ott M, Zitnick CL, Ma J, Fergus R. Organic construction and performance emerge from scaling unsupervised studying to 250 million protein sequences. Proceedings of the Nationwide Academy of Sciences. 2021 Apr 13;118(15):e2016239118.
41. Rao R, Meier J, Sercu T, Ovchinnikov S, Rives A. Transformer protein language fashions are unsupervised construction learners. Biorxiv. 2020 Dec 15:2020-12.
42. Rao RM, Liu J, Verkuil R, Meier J, Canny J, Abbeel P, Sercu T, Rives A. MSA transformer. InInternational convention on machine studying 2021 Jul 1 (pp. 8844-8856). PMLR.
43. Ji Y, Zhou Z, Liu H, Davuluri RV. DNABERT: pre-trained Bidirectional Encoder Representations from Transformers mannequin for DNA-language in genome. Bioinformatics. 2021 Aug 1;37(15):2112-20.
44. Jeffrey HJ. Chaos sport illustration of gene construction. Nucleic acids analysis. 1990 Apr 25;18(8):2163-70.
45. Illergård Ok, Ardell DH, Elofsson A. Construction is three to 10 occasions extra conserved than sequence—a research of structural response in protein cores. Proteins: Construction, Perform, and Bioinformatics. 2009 Nov 15;77(3):499-508.
46. Chothia C, Lesk AM. The relation between the divergence of sequence and construction in proteins. The EMBO journal. 1986 Apr 1;5(4):823-6.
47. Roel-Touris J, Don CG, V. Honorato R, Rodrigues JP, Bonvin AM. Much less is extra: coarse-grained integrative modeling of enormous biomolecular assemblies with HADDOCK. Journal of chemical principle and computation. 2019 Sep 20;15(11):6358-67.
48. Duong VT, Diessner EM, Grazioli G, Martin RW, Butts CT. Neural Upscaling from Residue-Degree Protein Construction Networks to Atomistic Buildings. Biomolecules. 2021 Nov 30;11(12):1788.
49. Jumper J, Evans R, Pritzel A, Inexperienced T, Figurnov M, Ronneberger O, Tunyasuvunakool Ok, Bates R, Žídek A, Potapenko A, Bridgland A. Extremely correct protein construction prediction with AlphaFold. nature. 2021 Aug 26;596(7873):583-9.
50. Marrink SJ, Risselada HJ, Yefimov S, Tieleman DP, De Vries AH. The MARTINI pressure area: coarse grained mannequin for biomolecular simulations. The journal of bodily chemistry B. 2007 Jul 12;111(27):7812-24.
51. Liwo A, Baranowski M, Czaplewski C, Gołaś E, He Y, Jagieła D, Krupa P, Maciejczyk M, Makowski M, Mozolewska MA, Niadzvedtski A. A unified coarse-grained mannequin of organic macromolecules primarily based on mean-field multipole–multipole interactions. Journal of molecular modeling. 2014 Aug;20(8):2306.
52. Cao F, von Bülow S, Tesei G, Lindorff‐Larsen Ok. A rough‐grained mannequin for disordered and multi‐area proteins. Protein Science. 2024 Nov;33(11):e5172.
53. Van Kempen M, Kim SS, Tumescheit C, Mirdita M, Lee J, Gilchrist CL, Söding J, Steinegger M. Quick and correct protein construction search with Foldseek. Nature biotechnology. 2024 Feb;42(2):243-6.
54. Wang R, Fang X, Lu Y, Yang CY, Wang S. The PDBbind database: methodologies and updates. Journal of medicinal chemistry. 2005 Jun 16;48(12):4111-9.
55. Ingraham J, Garg V, Barzilay R, Jaakkola T. Generative fashions for graph-based protein design. Advances in neural data processing methods. 2019;32.
56. Jing B, Eismann S, Suriana P, Townshend RJ, Dror R. Studying from protein construction with geometric vector perceptrons. arXiv preprint arXiv:2009.01411. 2020 Sep 3.
57. Ochiai T, Inukai T, Akiyama M, Furui Ok, Ohue M, Matsumori N, Inuki S, Uesugi M, Sunazuka T, Kikuchi Ok, Kakeya H. Variational autoencoder-based chemical latent area for giant molecular buildings with 3D complexity. Communications Chemistry. 2023 Nov 16;6(1):249.