Effects of multiple conformers per compound upon 3-D similarity search and bioassay data analysis

Abstract

Background: To improve the utility of PubChem, a public repository containing biological activities of small molecules, the PubChem3D project adds computationally-derived three-dimensional (3-D) descriptions to the small-molecule records contained in the PubChem Compound database and provides various search and analysis tools that exploit 3-D molecular similarity. Therefore, the efficient use of PubChem3D resources requires an understanding of the statistical and biological meaning of computed 3-D molecular similarity scores between molecules.

Results: The present study investigated effects of employing multiple conformers per compound upon the 3-D similarity scores between ten thousand randomly selected biologically-tested compounds (10-K set) and between non-inactive compounds in a given biological assay (156-K set). When the "best-conformer-pair" approach, in which a 3-D similarity score between two compounds is represented by the greatest similarity score among all possible conformer pairs arising from a compound pair, was employed with ten diverse conformers per compound, the average 3-D similarity scores for the 10-K set increased by 0.11, 0.09, 0.15, 0.16, 0.07, and 0.18 for STST-opt, CTST-opt, ComboTST-opt, STCT-opt, CTCT-opt, and ComboTCT-opt, respectively, relative to the corresponding averages computed using a single conformer per compound. Interestingly, the best-conformer-pair approach also increased the average 3-D similarity scores for the non-inactive-non-inactive (NN) pairs for a given assay, by comparable amounts to those for the random compound pairs, although some assays showed a pronounced increase in the per-assay NN-pair 3-D similarity scores, compared to the average increase for the random compound pairs.

Conclusion: These results suggest that the use of ten diverse conformers per compound in PubChem bioassay data analysis using 3-D molecular similarity is not expected to increase the separation of non-inactive from random and inactive spaces "on average", although some assays show a noticeable separation between the non-inactive and random spaces when multiple conformers are used for each compound. The present study is a critical next step to understand effects of conformational diversity of the molecules upon the 3-D molecular similarity and its application to biological activity data analysis in PubChem. The results of this study may be helpful to build search and analysis tools that exploit 3-D molecular similarity between compounds archived in PubChem and other molecular libraries in a more efficient way.

Figure 1

Similarity distributions for “single-conformer” (Scenario A) approach. Binned distributions in 0.01 increments of the 3-D similarity scores for the unique “conformer-conformer” pairs arising from 10,000 randomly selected biologically tested compounds (10-K set), computed using a single conformer per compound for (a) ST-optimized and (b) CT-optimized superpositions.

Figure 2

Similarity distributions for multi-conformer “all-conformer-pair” (Scenario B) approach. Binned distributions in 0.01 increments of the 3-D similarity scores for the unique “conformer-conformer” pairs arising from 10,000 randomly selected biologically tested compounds (10-K set), computed using ten diverse conformers per compound and the “all-conformer-pair” approach for (a) ST-optimized and (b) CT-optimized superpositions.

Figure 3

Similarity distributions for multi-conformer “best-conformer-pair” (Scenario D) approach. Binned distributions in 0.01 increments of the 3-D similarity scores for the unique “conformer-compound” pairs arising from 10,000 randomly selected biologically tested compounds (10-K set), computed using ten diverse conformers per compound and the “best-conformer-pair” approach for (a) ST-optimized and (b) CT-optimized superpositions.

Figure 4

Similarity distributions for multi-conformer “best-conformer-pair” (Scenario E) approach. Binned distributions in 0.01 increments of the 3-D similarity scores for the unique “compound-compound” pairs arising from 10,000 randomly selected biologically tested compounds (10-K set), computed using ten diverse conformers per compound and the “best-conformer-pair” approach for (a) ST-optimized and (b) CT-optimized superpositions.

Figure 5

Average and standard deviation distributions for shape-Tanimoto (ST), per “query”. Binned distributions in 0.01 increments of the average and standard deviation of the shape-Tanimoto (ST) scores per query-type for the five similarity search scenarios tested (see Table 1): Scenario A [(a) and (b)], Scenario B [(c) and (d)], Scenario C [(e) and (f)], Scenario D [(g) and (h)], and Scenario E [(i) and (j)]. The left panels [(a), (c), (e), (g), and (i)] are for the ST-optimized ST scores, and the right panels [(b), (d), (f), (h), and (j)] are for the CT-optimized ST scores.

Figure 6

Average and standard deviation distributions for color-Tanimoto (CT), per “query”. Binned distributions in 0.01 increments of the average and standard deviation of the color-Tanimoto (CT) scores per query-type for the five similarity search scenarios tested (see Table 1): Scenario A [(a) and (b)], Scenario B [(c) and (d)], Scenario C [(e) and (f)], Scenario D [(g) and (h)], and Scenario E [(i) and (j)]. The left panels [(a), (c), (e), (g), and (i)] are for the ST-optimized CT scores, and the right panels [(b), (d), (f), (h), and (j)] are for the CT-optimized CT scores.

Figure 7

Average and standard deviation distributions for combo-Tanimoto (ComboT), per “query”. Binned distributions in 0.01 increments of the average and standard deviation of the combo-Tanimoto (ComboT) scores per query-type for the five similarity search scenarios tested (see Table 1): Scenario A [(a) and (b)], Scenario B [(c) and (d)], Scenario C [(e) and (f)], Scenario D [(g) and (h)], and Scenario E [(i) and (j)]. The left panels [(a), (c), (e), (g), and (i)] are for the ST-optimized ComboT scores, and the right panels [(b), (d), (f), (h), and (j)] are for the CT-optimized ComboT scores.

Figure 8

Break down of assays by type. Assay-type counts for the 1,528 bioassays considered in the present study.

Figure 9

Per-AID shape-Tanimoto (ST)-optimized 3-D similarity average values. Binned distributions in 0.01 increments of the average 3-D similarity scores for non-inactive–non-inactive (NN) pairs of 1,528 AIDs in the PubChem BioAssay database, computed at the shape-Tanimoto-optimized superposition: (a) shape-Tanimoto (ST), (b) color-Tanimoto (CT), and (c) combo-Tanimoto (ComboT). “Single conformer”, “Multiple conformers (all)”, and “Multiple conformers (best)” correspond to search scenarios A, B, and E, respectively (See Table 1).

Figure 10

Per-AID color-Tanimoto (CT)-optimized 3-D similarity average values. Binned distributions in 0.01 increments of the average 3-D similarity scores for non-inactive–non-inactive (NN) pairs of 1,528 AIDs in the PubChem BioAssay database, computed at color-Tanimoto-optimized superposition: (a) shape-Tanimoto (ST), (b) color-Tanimoto (CT), and (c) combo-Tanimoto (ComboT). “Single conformer”, “Multiple conformers (all)”, and “Multiple conformers (best)” correspond to search scenarios A, B, and E, respectively (See Table 1).

Figure 11

Deviation from random of per-AID shape-Tanimoto (ST)-optimized 3-D similarity average values. Deviation of the ST-optimized 3-D similarity scores for non-inactive–non-inactive (NN) pairs of 1,528 AIDs from the corresponding average for the random compound pairs, computed using both a single conformer and best multiple (ten) diverse conformers per compound: (a) ST-optimized ST, (b) ST-optimized CT, and (c) ST-optimized ComboT. The deviations are binned with increment of 0.1 standard deviation (σ) unit. “Single” and “Multiple” refer to search scenarios A and E, respectively (See Table 1).

Figure 12

Deviation from random of per-AID color-Tanimoto (CT)-optimized 3-D similarity average values. Deviation of the CT-optimized 3-D similarity scores for non-inactive–non-inactive (NN) pairs of 1,528 AIDs from the corresponding average for the random compound pairs, computed using both a single conformer and best multiple (ten) diverse conformers per compound: (a) CT-optimized ST, (b) CT-optimized CT, and (c) CT-optimized ComboT. The deviations are binned with increment of 0.1 standard deviation (σ) unit. “Single” and “Multiple” refer to search scenarios A and E, respectively (See Table 1).

Figure 13

Demonstrated multi-conformer effects using AID 1033. Effects of employing multiple conformers per compound upon 3-D similarity of the non-inactive compounds tested in AID 1033. Eight compounds in panel (a) are non-inactive in AID 1033. Panel (b) depicts the dendrogram that shows the 2-D similarity among the eight structures, computed using the PubChem subgraph fingerprints. The dendrograms for the 3-D shape-optimized combo-Tanimoto (ComboT^ST-opt) similarity are shown in panels (c) and (d) for a single conformer per compound and ten diverse conformers per compound, respectively. Panel (e) compares conformer superpositions between two of the non-inactive compounds (CIDs 668798 and 1246750). LID stands for the local identifier, which represents different conformers of a compound.

Figure 14

Demonstrated multi-conformer effects using AID 491. Effects of employing multiple conformers per compound upon 3-D similarity of non-inactive compounds tested in AID 491. Panel (a) shows the dendrogram based on 2-D similarity among eight compounds selected from 60 non-inactive compounds in AID 491. The dendrograms for the 3-D shape-optimized combo-Tanimoto (ComboT^ST-opt) similarity are shown in panels (b) and (c) for a single conformer per compound and ten diverse conformers per compound, respectively. Panel (d) compares conformer superpositions between two of the non-inactive compounds (CIDs 490518 and 505938). LID stands for the local conformer identifier, which represents different conformers of a compound.

Figure 15

Summary comparison of overall average similarity. Comparison of the overall average 3-D similarity scores, μμ(XT)], for the non-inactive–non-inactive (NN) pairs with those for the non-inactive–inactive (NI) pairs and random compound pairs. The words, “Single”, “Best”, and “All”, in the legend box indicate the single-conformer approach (Scenario A), “best-conformer-pair” approach (Scenario E), and “all-conformer-pair” approach (Scenario B), respectively. Study A is the present study, and Study B is a previous study by Kim et al. (Ref. [10]).